Show of Hands:Votes: Party classification:: Q109244Yes_AllX3_cnk01_rest
bdanalytics
Date: (Thu) Jun 30, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSlt
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glbMdlSlt vs. glbMdlFnlNsl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
# '(glbObsAll[, "Q109244"] != "")' # NA
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "Yes"))' # Yes
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(4, 6, 7, 8, 9, 10, 16, 32, 64, 128, 253) # accuracy(8) = 0.5648
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["All.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# , "lda" # error: model fit failed for Fold1.Rep1: parameter=none Error in lda.default(x, grouping, ...)
# ,"lda2" # error: There were missing values in resampled performance measures.
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlSequential <- c(NULL
, "All.X#zv.pca#rcv#glmnet"
)
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame() # alpha shd be <= 1.0 ALWAYS
,data.frame(parameter = "alpha", vals = "0.325 0.550 0.775 0.9 1.000")
,data.frame(parameter = "lambda", vals = "1.034113e-03 4.799925e-03 2.227928e-02 0.04 0.06")
) # max.Accuracy.OOB = 0.7875648 @ 0.55 0.04
# AllX_nzv_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "1.842462e-02 0.03287977 0.04733492 0.06179007 0.07624522")) # max.Accuracy.OOB = 0.7875648 @ 0.55 0.06179007 @ 0.55 0.04733492 @ 0.775 0.03287977 @ 1 0.01842462
# AllX_zvpca_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "1.847495e-02 0.02 0.03296959 0.04 0.05")) # max.Accuracy.OOB = 0.7927461 @ 1 0.01847495
# # 0.7875648 @ 0.775 0.03296959
#
glbMdlTuneParams <- rbind(glbMdlTuneParams
,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "All.X#nzv#rcv#glmnet"), AllX_nzv_rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "All.X#zv.pca#rcv#glmnet"),
# AllX_zvpca_rcv_glmnetTuneParams)
)
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Trn.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL # NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.YeoJohnson.pca")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSltId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFnlNslId <- NULL #select from c(NULL, glbMdlSltId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutDf) {
# require(tidyr)
# obsOutDf <- obsOutDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutDf) {
# }
)
#obsOutFnlNslDf <- savobsOutFnlNslDf
# glbObsOut$mapFn <- function(obsOutDf) {
# txfout_df <- dplyr::select(obsOutDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFnlNslDf <- glbObsOut$mapFn(obsOutFnlNslDf); print(head(obsOutFnlNslDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
glbOutStackFnames <- # NULL #: default
c("Q109244NA_Ensemble_cnk03_rest_out_fin.csv")
# c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244Yes_AllX3_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "cluster.data" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Q109244Yes_AllX3_cnk01_manage.missing.data_manage.missing.data.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Trn..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 cluster.data 1 0 0 14.447 NA NAStep 1.0: cluster data
chunk option: eval=
Step 1.0: cluster data
Step 1.0: cluster data
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
## Loading required package: dynamicTreeCut## Loading required package: entropy## Loading required package: tidyr## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero## abs.cor.y
## Q121699.fctr 0.06186040
## Q114517.fctr 0.06233932
## Q124122.fctr 0.06976947
## Q114386.fctr 0.07613008
## Q114152.fctr 0.07783674
## [1] " .rnorm abs(cor): 0.0102"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.4974"## Loading required package: lazyeval## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 40 9 0.4769183 49
## 2 MKn 1 MKn_1 97 26 0.5157821 123
## 3 MKy 1 MKy_1 186 43 0.4829775 229
## 4 PKn 1 PKn_1 45 5 0.3250830 50
## 5 PKy 1 PKy_1 10 1 0.3046361 11
## 6 SKn 1 SKn_1 325 92 0.5276960 417
## 7 SKy 1 SKy_1 39 7 0.4264615 46
## [1] "glbObsAll$Hhold.fctr Entropy: 0.4937 (99.2605 pct)"## [1] "Category: N"
## [1] "lclgetMatrixSimilarity: duration: 1.822000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :-0.02740 Min. :0.8351 Min. :0.8561
## 1st Qu.: 0.04078 1st Qu.:0.8653 1st Qu.:0.8813
## Median : 0.06237 Median :0.8988 Median :0.9141
## Mean : 0.06185 Mean :0.8918 Mean :0.9055
## 3rd Qu.: 0.08213 3rd Qu.:0.9239 3rd Qu.:0.9319
## Max. : 0.13018 Max. :0.9290 Max. :0.9365
## [1] " variance:"
## cosine correlation mywgtCosine
## 0.0010705761 0.0010416318 0.0007120649
## [1] "selected similarity metric for clustering: cosine"
## [1] "max distance(0.2961) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4156 5186 D N NA NA NA
## 4978 6219 D N NA NA No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4156 NA NA NA NA NA
## 4978 NA Pt Yes No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4156 NA NA NA NA Yes
## 4978 No Yes NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4156 Art Yes NA NA No
## 4978 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4156 NA Giving NA No Yes
## 4978 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4156 Yes Id No Standard hours Cool headed
## 4978 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4156 Yes NA No NA NA
## 4978 NA NA Yes NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4156 NA NA NA NA NA
## 4978 NA NA Yes Start No
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4156 NA NA NA NA NA
## 4978 No Cs Yes No Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4156 NA NA NA NA NA
## 4978 Yes No TMI NA Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4156 NA NA NA NA NA
## 4978 Tunes Technology No Yes No
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4156 NA NA NA NA NA
## 4978 NA Yes NA Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4156 No Yes Risk-friendly Yes! NA
## 4978 NA Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4156 NA Yes In-person NA No
## 4978 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4156 Yes NA Yy NA NA
## 4978 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4156 NA NA Yes Yes Yes
## 4978 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4156 No No NA Yes Yes
## 4978 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4156 NA NA NA NA NA
## 4978 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4156 NA NA Yes NA NA
## 4978 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4156 NA NA Yes Yes Yes
## 4978 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4156 Yes NA Yes
## 4978 NA NA NA
## [1] "min distance(0.0248) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 406 504 D N No Yes No
## 4654 5812 D N No Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 406 No Pt Yes No No
## 4654 No Pc Yes No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 406 No Yes No No Yes
## 4654 No Yes No No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 406 Science Yes Study first Yes Yes
## 4654 Science Yes Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 406 Yes Giving No Yes No
## 4654 Yes Giving Yes No No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 406 Yes Pr No NA Cool headed
## 4654 Yes Pr No Odd hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 406 Yes Happy No Yes No
## 4654 No Happy Yes Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 406 No A.M. Yes End Yes
## 4654 No P.M. No Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 406 Yes Me Yes No Yes
## 4654 No Cs Yes No No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 406 No No Mysterious Yes Yes
## 4654 No No Mysterious Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 406 Tunes Technology No No Yes
## 4654 Talk People Yes No No
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 406 Yes Yes Demanding Yes Mac
## 4654 Yes Yes Demanding No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 406 Yes Yes Risk-friendly Yes! No
## 4654 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 406 Space No In-person No Yes
## 4654 Space Yes In-person No No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 406 Yes Yes Gr Yes No
## 4654 Yes Yes Yy Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 406 Yes Yes Yes No Yes
## 4654 Yes No No No Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 406 No No No No No
## 4654 No No No Yes Yes
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 406 Own Optimist Mom Yes Yes
## 4654 Rent Optimist Dad Yes No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 406 No Yes Yes Check! No
## 4654 No Yes Yes Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 406 No Yes Yes Yes No
## 4654 No Yes Yes Yes Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 406 Yes No Yes
## 4654 Yes No Yes
## [1] "Category: MKn"
## [1] "lclgetMatrixSimilarity: duration: 6.127000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :-0.01495 Min. :0.8547 Min. :0.8710
## 1st Qu.: 0.08784 1st Qu.:0.9042 1st Qu.:0.9153
## Median : 0.11679 Median :0.9358 Median :0.9402
## Mean : 0.11025 Mean :0.9224 Mean :0.9292
## 3rd Qu.: 0.13840 3rd Qu.:0.9431 3rd Qu.:0.9470
## Max. : 0.19552 Max. :0.9564 Max. :0.9589
## NA's :1
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0018872986 0.0007094848 0.0005114007
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(10.0000) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 7 11 D MKn NA NA NA
## 3363 4185 D MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 7 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 7 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 7 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 7 NA NA No No Yes
## 3363 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 7 No Pr No Odd hours Cool headed
## 3363 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 7 No Happy No Yes No
## 3363 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 7 Yes P.M. Yes End Yes
## 3363 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 7 No Me Yes Yes Yes
## 3363 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 7 No NA NA NA NA
## 3363 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 7 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 7 NA NA Supportive No PC
## 3363 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 7 Yes Yes Cautious Umm... No
## 3363 NA Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 7 NA No Online Yes NA
## 3363 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 7 Yes No Gr Yes No
## 3363 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 7 Yes NA Yes No No
## 3363 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 7 Yes Yes No No NA
## 3363 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 7 Own Pessimist NA NA Yes
## 3363 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 7 Yes Yes Yes NA NA
## 3363 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 7 NA NA No No Yes
## 3363 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 7 Yes No Yes
## 3363 NA NA NA
## [1] "min distance(0.2084) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1091 1351 D MKn NA NA NA
## 5755 969 <NA> MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1091 NA NA NA NA NA
## 5755 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1091 NA No Mysterious No No
## 5755 NA NA NA No No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1091 Tunes People No No No
## 5755 Talk People No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1091 Yes Yes Supportive No PC
## 5755 Yes Yes Supportive NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1091 No Yes Cautious Yes! No
## 5755 Yes Yes Cautious Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1091 Space No In-person No No
## 5755 Space Yes In-person Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1091 Yes Yes Yy Yes Yes
## 5755 Yes Yes Gr Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1091 No No No Yes No
## 5755 No Yes Yes Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1091 Yes No No No No
## 5755 Yes No No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1091 Own Optimist Mom No No
## 5755 Rent Pessimist Dad Yes No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1091 No Yes Yes Nope No
## 5755 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1091 No Yes Yes No No
## 5755 Yes Yes No Yes No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1091 Yes No Yes
## 5755 Yes Yes Yes
## [1] "Category: MKy"
## [1] "lclgetMatrixSimilarity: duration: 11.214000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :-0.04728 Min. :0.8507 Min. :0.8622
## 1st Qu.: 0.07995 1st Qu.:0.9071 1st Qu.:0.9165
## Median : 0.11698 Median :0.9368 Median :0.9399
## Mean : 0.11189 Mean :0.9223 Mean :0.9285
## 3rd Qu.: 0.14774 3rd Qu.:0.9439 3rd Qu.:0.9473
## Max. : 0.20582 Max. :0.9477 Max. :0.9512
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0022008451 0.0007894852 0.0005878753
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(1.8004) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1787 2215 D MKy No No No
## 4460 5564 D MKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1787 No Pc Yes No No
## 4460 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1787 No No No No Yes
## 4460 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1787 Science Yes Study first Yes Yes
## 4460 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1787 Yes Giving No Yes No
## 4460 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1787 No Pr No NA Cool headed
## 4460 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1787 Yes Happy Yes Yes No
## 4460 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1787 No A.M. Yes Start Yes
## 4460 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1787 No Cs Yes Yes No
## 4460 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1787 No Yes Mysterious Yes Yes
## 4460 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1787 Talk People No Yes Yes
## 4460 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1787 Yes Yes Supportive No PC
## 4460 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1787 No Yes NA NA NA
## 4460 No Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1787 NA NA In-person No NA
## 4460 Space No In-person No Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1787 NA NA NA NA NA
## 4460 Yes Yes Yy Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1787 NA NA NA NA NA
## 4460 No No No Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1787 NA NA NA NA NA
## 4460 No No No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1787 NA NA NA NA NA
## 4460 Rent Optimist Mom No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1787 NA NA NA NA NA
## 4460 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1787 NA NA NA NA NA
## 4460 Yes Yes Yes Yes No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1787 NA NA NA
## 4460 Yes Yes Yes
## [1] "min distance(0.1265) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 132 167 D MKy NA NA NA
## 1449 1800 D MKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 132 NA NA NA NA NA
## 1449 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 132 NA NA NA No Yes
## 1449 NA NA NA No Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 132 No Yes Demanding No Mac
## 1449 No NA Supportive NA PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 132 Yes Yes Cautious Yes! No
## 1449 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 132 Space No Online Yes Yes
## 1449 Space No Online Yes Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 132 No Yes Gr Yes No
## 1449 Yes Yes Gr Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 132 Yes No Yes Yes Yes
## 1449 No Yes Yes Yes No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 132 Yes Yes No Yes Yes
## 1449 Yes No Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 132 Own Pessimist Mom Yes Yes
## 1449 Own Pessimist Dad Yes Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 132 Yes No Yes Check! No
## 1449 Yes No Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 132 No Yes No No No
## 1449 No No Yes No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 132 Yes Yes Yes
## 1449 Yes Yes Yes
## [1] "Category: PKn"
## [1] "lclgetMatrixSimilarity: duration: 1.535000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :0.004614 Min. :0.8391 Min. :0.8583
## 1st Qu.:0.058984 1st Qu.:0.8800 1st Qu.:0.8957
## Median :0.080125 Median :0.9128 Median :0.9215
## Mean :0.082727 Mean :0.9022 Mean :0.9134
## 3rd Qu.:0.114004 3rd Qu.:0.9310 3rd Qu.:0.9377
## Max. :0.166395 Max. :0.9362 Max. :0.9420
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0012004045 0.0009680793 0.0006665449
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(1.5087) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1181 1460 D PKn Yes Yes Yes
## 4959 6196 D PKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1181 No Pt No No No
## 4959 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1181 No Yes No Yes Yes
## 4959 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1181 Art Yes NA NA NA
## 4959 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1181 No Giving Yes NA NA
## 4959 NA NA Yes Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1181 NA NA NA NA NA
## 4959 No Id No Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1181 NA NA NA Yes Yes
## 4959 Yes Happy Yes NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1181 No P.M. Yes End Yes
## 4959 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1181 No Cs Yes Yes No
## 4959 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1181 Yes No Mysterious NA NA
## 4959 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1181 Talk People Yes Yes Yes
## 4959 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1181 NA NA Supportive Yes Mac
## 4959 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1181 No Yes Risk-friendly NA NA
## 4959 No Yes Cautious Yes! Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1181 NA NA NA NA NA
## 4959 Space No In-person No Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1181 Yes Yes Yy Yes Yes
## 4959 Yes NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1181 Yes No Yes Yes Yes
## 4959 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1181 Yes No No Yes Yes
## 4959 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1181 Own Optimist Mom Yes Yes
## 4959 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1181 Yes Yes Yes Nope No
## 4959 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1181 No Yes Yes Yes No
## 4959 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1181 Yes Yes NA
## 4959 NA NA NA
## [1] "min distance(0.2597) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 848 1046 D PKn NA NA NA
## 3463 4312 D PKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 848 Yes Yes Risk-friendly Yes! No
## 3463 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 848 Space No In-person Yes NA
## 3463 Space Yes NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 848 NA NA No
## 3463 NA NA NA
## [1] "Category: PKy"
## [1] "lclgetMatrixSimilarity: duration: 0.368000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :0.02563 Min. :0.8519 Min. :0.8691
## 1st Qu.:0.08875 1st Qu.:0.9027 1st Qu.:0.9175
## Median :0.13003 Median :0.9299 Median :0.9342
## Mean :0.10965 Mean :0.9190 Mean :0.9281
## 3rd Qu.:0.14038 3rd Qu.:0.9398 3rd Qu.:0.9445
## Max. :0.16914 Max. :0.9407 Max. :0.9479
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0022513416 0.0007094706 0.0004915530
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(1.2004) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 356 446 D PKy Yes Yes No
## 3531 4399 D PKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 356 No Pc Yes No No
## 3531 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 356 No Yes Yes No No
## 3531 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 356 Science No Study first No No
## 3531 Art NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 356 No Giving No Yes Yes
## 3531 NA NA No No NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 356 No Id No Standard hours Cool headed
## 3531 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 356 No Happy Yes Yes No
## 3531 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 356 Yes A.M. No Start Yes
## 3531 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 356 Yes Me NA NA NA
## 3531 NA NA Yes No No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 356 NA NA Mysterious Yes Yes
## 3531 No No Mysterious Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 356 Tunes Technology Yes No Yes
## 3531 NA NA NA Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 356 Yes No Demanding No PC
## 3531 No Yes Supportive No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 356 Yes Yes Risk-friendly NA NA
## 3531 Yes Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 356 NA NA In-person Yes Yes
## 3531 Space No Online No No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 356 Yes Yes Yy Yes No
## 3531 Yes Yes Gr Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 356 No No No Yes No
## 3531 No No Yes No No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 356 No No NA NA NA
## 3531 Yes Yes No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 356 NA NA NA NA NA
## 3531 Own Pessimist Dad No No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 356 NA NA NA NA NA
## 3531 Yes No No Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 356 No Yes Yes No No
## 3531 No Yes Yes No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 356 Only-child No Yes
## 3531 Yes Yes Yes
## [1] "min distance(0.5671) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1242 1537 D PKy NA NA NA
## 3531 4399 D PKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1242 NA NA NA NA NA
## 3531 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1242 NA NA Yes Yes Yes
## 3531 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1242 Science No Study first No Yes
## 3531 Art NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1242 Yes Giving Yes Yes No
## 3531 NA NA No No NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1242 No Id No Odd hours Hot headed
## 3531 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1242 Yes Happy No Yes No
## 3531 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1242 Yes NA No NA Yes
## 3531 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1242 Yes NA Yes Yes Yes
## 3531 NA NA Yes No No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1242 Yes No TMI Yes No
## 3531 No No Mysterious Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1242 Tunes People Yes Yes Yes
## 3531 NA NA NA Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1242 No Yes Demanding No PC
## 3531 No Yes Supportive No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1242 Yes Yes Cautious Umm... No
## 3531 Yes Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1242 Socialize No Online Yes Yes
## 3531 Space No Online No No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1242 Yes Yes Yy Yes No
## 3531 Yes Yes Gr Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1242 No Yes No Yes No
## 3531 No No Yes No No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1242 Yes Yes No Yes No
## 3531 Yes Yes No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1242 Own Optimist Dad Yes Yes
## 3531 Own Pessimist Dad No No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1242 Yes Yes No Check! No
## 3531 Yes No No Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1242 Yes Yes Yes No Yes
## 3531 No Yes Yes No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1242 Yes Yes No
## 3531 Yes Yes Yes
## [1] "Category: SKn"
## [1] "lclgetMatrixSimilarity: duration: 33.359000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :-0.03524 Min. :0.8360 Min. :0.8598
## 1st Qu.: 0.05349 1st Qu.:0.8867 1st Qu.:0.8988
## Median : 0.07919 Median :0.9263 Median :0.9318
## Mean : 0.07861 Mean :0.9110 Mean :0.9200
## 3rd Qu.: 0.10583 3rd Qu.:0.9368 3rd Qu.:0.9415
## Max. : 0.17287 Max. :0.9412 Max. :0.9461
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0013335314 0.0008981954 0.0006223784
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(1.6867) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1491 1848 D SKn NA NA NA
## 6810 6222 <NA> SKn NA Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1491 NA NA NA NA NA
## 6810 No Pt No Yes Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1491 NA NA NA NA NA
## 6810 No Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1491 NA NA NA NA NA
## 6810 Art Yes NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1491 NA NA NA NA NA
## 6810 NA NA Yes Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1491 NA NA NA NA NA
## 6810 Yes Pr No Standard hours Hot headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1491 NA NA NA NA NA
## 6810 Yes Happy Yes No Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1491 NA NA NA NA NA
## 6810 No A.M. No End Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1491 NA NA NA NA NA
## 6810 No Cs Yes NA Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1491 NA NA NA NA NA
## 6810 Yes Yes Mysterious NA No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1491 NA NA NA NA NA
## 6810 Talk Technology No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1491 NA NA NA NA NA
## 6810 Yes Yes NA Yes PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1491 No Yes Cautious Yes! No
## 6810 Yes Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1491 Space No In-person No No
## 6810 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1491 Yes Yes Yy No No
## 6810 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1491 No No No Yes No
## 6810 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1491 No No No Yes No
## 6810 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1491 Rent Optimist Dad No Yes
## 6810 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1491 No Yes Yes Nope No
## 6810 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1491 No Yes Yes Yes No
## 6810 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1491 Yes No No
## 6810 NA NA NA
## [1] "min distance(0.1438) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1491 1848 D SKn NA NA NA
## 1826 2263 R SKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA No NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA Yes NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1491 NA NA NA NA NA
## 1826 NA NA Demanding No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1491 No Yes Cautious Yes! No
## 1826 No Yes Cautious Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1491 Space No In-person No No
## 1826 Space Yes In-person Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1491 Yes Yes Yy No No
## 1826 Yes Yes Gr Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1491 No No No Yes No
## 1826 No No No Yes No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1491 No No No Yes No
## 1826 No No No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1491 Rent Optimist Dad No Yes
## 1826 Own Pessimist Dad No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1491 No Yes Yes Nope No
## 1826 Yes Yes No Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1491 No Yes Yes Yes No
## 1826 No Yes Yes Yes Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1491 Yes No No
## 1826 Yes Yes Yes
## [1] "Category: SKy"
## [1] "lclgetMatrixSimilarity: duration: 1.405000 secs"
## [1] " Similarity stats:"
## correlation cosine mywgtCosine
## Min. :-0.02069 Min. :0.8550 Min. :0.8729
## 1st Qu.: 0.04640 1st Qu.:0.9003 1st Qu.:0.9132
## Median : 0.08407 Median :0.9299 Median :0.9370
## Mean : 0.07829 Mean :0.9169 Mean :0.9261
## 3rd Qu.: 0.10793 3rd Qu.:0.9387 3rd Qu.:0.9450
## Max. : 0.17242 Max. :0.9440 Max. :0.9528
## [1] " variance:"
## correlation cosine mywgtCosine
## 0.0017074361 0.0008073667 0.0005726942
## [1] "selected similarity metric for clustering: correlation"
## [1] "max distance(1.5510) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2710 3373 D SKy NA NA NA
## 3205 3994 D SKy NA Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2710 NA NA NA NA NA
## 3205 No Pc Yes No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2710 NA NA NA NA NA
## 3205 No Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2710 NA NA NA NA NA
## 3205 Science Yes Study first No No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2710 NA NA NA NA NA
## 3205 Yes Giving Yes Yes NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2710 NA NA NA NA NA
## 3205 NA NA NA Standard hours NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2710 NA NA NA NA NA
## 3205 Yes NA Yes Yes Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2710 NA NA NA NA NA
## 3205 No P.M. Yes End No
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2710 NA NA NA NA NA
## 3205 No Me No No No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2710 NA NA Mysterious No Yes
## 3205 No No Mysterious NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2710 Tunes Technology No Yes Yes
## 3205 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2710 No Yes Supportive Yes Mac
## 3205 NA NA Supportive NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2710 No Yes Risk-friendly Umm... Yes
## 3205 Yes Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2710 Space No Online Yes Yes
## 3205 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2710 Yes Yes Gr Yes No
## 3205 NA No Yy NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2710 No Yes NA NA NA
## 3205 NA NA Yes No NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2710 NA NA NA NA NA
## 3205 Yes Yes Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2710 NA NA NA NA NA
## 3205 Own Optimist Dad No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2710 NA NA NA NA NA
## 3205 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2710 NA No No NA NA
## 3205 No Yes Yes No Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2710 NA NA NA
## 3205 Yes Yes NA
## [1] "min distance(0.3370) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4128 5148 R SKy NA NA NA
## 6183 3124 <NA> SKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4128 NA NA NA NA NA
## 6183 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4128 NA NA NA NA NA
## 6183 NA NA NA NA Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4128 NA NA NA No No
## 6183 Science NA Study first No No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4128 No Giving Yes Yes Yes
## 6183 Yes Giving Yes Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4128 Yes Pr Yes Standard hours Hot headed
## 6183 No Id No Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4128 No Happy Yes Yes Yes
## 6183 No Happy Yes No Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4128 No P.M. Yes Start Yes
## 6183 Yes P.M. Yes End Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4128 No Me No No Yes
## 6183 No Me Yes Yes Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4128 Yes No Mysterious No No
## 6183 Yes No Mysterious No No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4128 Tunes Technology No No Yes
## 6183 Tunes People No No Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4128 No Yes Demanding NA PC
## 6183 No Yes Demanding No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4128 Yes Yes Cautious Umm... Yes
## 6183 Yes Yes Cautious Yes! Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4128 Space Yes Online Yes No
## 6183 Space No In-person Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4128 Yes Yes Yy Yes No
## 6183 Yes Yes Yy No No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4128 No No Yes Yes No
## 6183 No Yes No No No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4128 No Yes Yes Yes No
## 6183 Yes Yes Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4128 Rent Pessimist Mom No Yes
## 6183 Rent Optimist Mom No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4128 Yes Yes No Check! Yes
## 6183 Yes Yes No Nope Yes
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4128 No Yes Yes No No
## 6183 Yes Yes No No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4128 Yes Yes No
## 6183 Yes No No
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 32 7 0.4706150 39
## 2 N 2 N_2 5 0 0.0000000 5
## 3 N 3 N_3 3 2 0.6730117 5
## 4 MKn 1 MKn_1 71 18 0.5035020 89
## 5 MKn 2 MKn_2 26 8 0.5455946 34
## 6 MKy 1 MKy_1 119 29 0.4947307 148
## 7 MKy 2 MKy_2 67 14 0.4603602 81
## 8 PKn 1 PKn_1 18 1 0.2061921 19
## 9 PKn 2 PKn_2 9 0 0.0000000 9
## 10 PKn 3 PKn_3 8 1 0.3488321 9
## 11 PKn 4 PKn_4 5 2 0.5982696 7
## 12 PKn 5 PKn_5 5 1 0.4505612 6
## 13 PKy 1 PKy_1 6 1 0.4101163 7
## 14 PKy 2 PKy_2 4 0 0.0000000 4
## 15 SKn 1 SKn_1 101 22 0.4696632 123
## 16 SKn 2 SKn_2 75 29 0.5918602 104
## 17 SKn 3 SKn_3 58 10 0.4175732 68
## 18 SKn 4 SKn_4 47 13 0.5226566 60
## 19 SKn 5 SKn_5 44 18 0.6024403 62
## 20 SKy 1 SKy_1 14 3 0.4659993 17
## 21 SKy 2 SKy_2 6 2 0.5623351 8
## 22 SKy 3 SKy_3 6 0 0.0000000 6
## 23 SKy 4 SKy_4 6 0 0.0000000 6
## 24 SKy 5 SKy_5 3 2 0.6730117 5
## 25 SKy 6 SKy_6 4 0 0.0000000 4
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.4807 (97.3591 pct)"## label step_major step_minor label_minor bgn end
## 1 cluster.data 1 0 0 14.447 98.452
## 2 partition.data.training 2 0 0 98.453 NA
## elapsed
## 1 84.005
## 2 NAStep 2.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Loading required package: reshape2## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 6.72 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 6.73 secs"## Warning in cor(mtxObsTrn, as.numeric(glbObsTrn[, glb_rsp_var]), use =
## "pairwise.complete.obs"): the standard deviation is zero## [1] "lclgetMatrixSimilarity: duration: 42.583000 secs"
## [1] "TrnNewSimilarity stats:"
## correlation cosine mywgtCosine
## Min. :1 Min. :0.04962 Min. :1
## 1st Qu.:1 1st Qu.:0.95114 1st Qu.:1
## Median :1 Median :0.95125 Median :1
## Mean :1 Mean :0.91532 Mean :1
## 3rd Qu.:1 3rd Qu.:0.95128 3rd Qu.:1
## Max. :1 Max. :0.95133 Max. :1
## NA's :37 NA's :37
## [1] " variance:"
## cosine correlation mywgtCosine
## 3.095312e-02 6.341140e-12 4.411291e-12
## [1] "selected similarity metric for partitioning: cosine"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## Stratum 1
##
## Population total and number of selected units: 40 7
## Stratum 2
##
## Population total and number of selected units: 97 18
## Stratum 3
##
## Population total and number of selected units: 186 37
## Stratum 4
##
## Population total and number of selected units: 45 7
## Stratum 5
##
## Population total and number of selected units: 10 2
## Stratum 6
##
## Population total and number of selected units: 325 74
## Stratum 7
##
## Population total and number of selected units: 39 7
## Stratum 8
##
## Population total and number of selected units: 9 2
## Stratum 9
##
## Population total and number of selected units: 26 5
## Stratum 10
##
## Population total and number of selected units: 43 10
## Stratum 11
##
## Population total and number of selected units: 5 2
## Stratum 12
##
## Population total and number of selected units: 1 1
## Stratum 13
##
## Population total and number of selected units: 92 19
## Stratum 14
##
## Population total and number of selected units: 7 2
## Number of strata 14
## Total number of selected units 193
## [1] "lclgetMatrixSimilarity: duration: 18.384000 secs"
## [1] "lclgetMatrixSimilarity: duration: 8.531000 secs"
## [1] "lclgetMatrixSimilarity: duration: 31.844000 secs"
## [1] "Similarity of partitions:"
## correlation cosine mywgtCosine obs.x obs.y
## 1 0.9999868 0.9499923 0.9999924 OOB Fit
## 2 0.9999870 0.9512085 0.9999925 OOB New
## 3 0.9999873 0.9058551 0.9999927 Fit New## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 115.48 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 223
## Fit 590 142 NA
## OOB 152 41 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.8060109 0.1939891 NA
## OOB 0.7875648 0.2124352 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 324 93 110 0.44262295 0.48186528
## 2 MKy 182 47 55 0.24863388 0.24352332
## 1 MKn 100 23 26 0.13661202 0.11917098
## 4 PKn 41 9 10 0.05601093 0.04663212
## 3 N 40 9 10 0.05464481 0.04663212
## 7 SKy 37 9 10 0.05054645 0.04663212
## 5 PKy 8 3 2 0.01092896 0.01554404
## .freqRatio.Tst
## 6 0.49327354
## 2 0.24663677
## 1 0.11659193
## 4 0.04484305
## 3 0.04484305
## 7 0.04484305
## 5 0.00896861## [1] "glbObsAll: "## [1] 1148 222## [1] "glbObsTrn: "## [1] 925 222## [1] "glbObsFit: "## [1] 732 221## [1] "glbObsOOB: "## [1] 193 221## [1] "glbObsNew: "## [1] 223 221## [1] "partition.data.training chunk: teardown: elapsed: 116.25 secs"## label step_major step_minor label_minor bgn
## 2 partition.data.training 2 0 0 98.453
## 3 select.features 3 0 0 214.858
## end elapsed
## 2 214.858 116.405
## 3 NA NAStep 3.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## [1] "cor(Q121699.fctr, Q121700.fctr)=0.7060"
## [1] "cor(Party.fctr, Q121699.fctr)=-0.0619"
## [1] "cor(Party.fctr, Q121700.fctr)=-0.0137"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q121700.fctr as highly correlated with Q121699.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## YOB 5.385909e-02 1 5.385909e-02 <NA>
## .clusterid 4.131607e-02 1 4.131607e-02 <NA>
## .clusterid.fctr 4.131607e-02 0 4.131607e-02 <NA>
## Gender.fctr 2.915818e-02 0 2.915818e-02 <NA>
## Q108754.fctr 2.873095e-02 0 2.873095e-02 <NA>
## Q108856.fctr 2.598500e-02 0 2.598500e-02 <NA>
## Q120014.fctr 2.540969e-02 0 2.540969e-02 <NA>
## Q115611.fctr 2.414298e-02 0 2.414298e-02 <NA>
## Q102906.fctr 2.383912e-02 0 2.383912e-02 <NA>
## Q101596.fctr 2.350567e-02 0 2.350567e-02 <NA>
## .pos 2.273256e-02 1 2.273256e-02 <NA>
## USER_ID 2.272035e-02 1 2.272035e-02 <NA>
## Q120194.fctr 2.095979e-02 0 2.095979e-02 <NA>
## Hhold.fctr 1.860696e-02 0 1.860696e-02 <NA>
## Q99480.fctr 1.780216e-02 0 1.780216e-02 <NA>
## Q108343.fctr 1.682882e-02 0 1.682882e-02 <NA>
## Q108617.fctr 1.602682e-02 0 1.602682e-02 <NA>
## Q108855.fctr 1.395626e-02 0 1.395626e-02 <NA>
## Q109367.fctr 1.103777e-02 0 1.103777e-02 <NA>
## Q117193.fctr 1.069452e-02 0 1.069452e-02 <NA>
## Q99982.fctr 9.622502e-03 0 9.622502e-03 <NA>
## Q114748.fctr 7.753180e-03 0 7.753180e-03 <NA>
## Q111580.fctr 7.655353e-03 0 7.655353e-03 <NA>
## Q98197.fctr 6.126550e-03 0 6.126550e-03 <NA>
## Q101163.fctr 5.914503e-03 0 5.914503e-03 <NA>
## Q102289.fctr 5.885855e-03 0 5.885855e-03 <NA>
## Q116881.fctr 5.319134e-03 0 5.319134e-03 <NA>
## Q101162.fctr 3.836084e-03 0 3.836084e-03 <NA>
## Q102674.fctr 3.017088e-03 0 3.017088e-03 <NA>
## Q102089.fctr 2.323547e-03 0 2.323547e-03 <NA>
## Q118232.fctr 2.165304e-03 0 2.165304e-03 <NA>
## Q118117.fctr 6.721935e-04 0 6.721935e-04 <NA>
## Q99581.fctr 1.641131e-05 0 1.641131e-05 <NA>
## Q108342.fctr -1.165784e-04 0 1.165784e-04 <NA>
## Q113584.fctr -2.536542e-04 0 2.536542e-04 <NA>
## Edn.fctr -4.631990e-04 0 4.631990e-04 <NA>
## Q113181.fctr -2.987810e-03 0 2.987810e-03 <NA>
## Q115899.fctr -3.391939e-03 0 3.391939e-03 <NA>
## Q122771.fctr -3.593507e-03 0 3.593507e-03 <NA>
## Q106388.fctr -4.089574e-03 0 4.089574e-03 <NA>
## Q113583.fctr -5.119777e-03 0 5.119777e-03 <NA>
## Q119334.fctr -5.832642e-03 0 5.832642e-03 <NA>
## Q105655.fctr -5.967822e-03 0 5.967822e-03 <NA>
## Q115777.fctr -6.671644e-03 0 6.671644e-03 <NA>
## Q98869.fctr -7.300228e-03 0 7.300228e-03 <NA>
## Q115602.fctr -8.572164e-03 0 8.572164e-03 <NA>
## Q107869.fctr -8.710476e-03 0 8.710476e-03 <NA>
## .rnorm -1.022887e-02 0 1.022887e-02 <NA>
## Q120472.fctr -1.117239e-02 0 1.117239e-02 <NA>
## Q100562.fctr -1.304301e-02 0 1.304301e-02 <NA>
## Q115610.fctr -1.308619e-02 0 1.308619e-02 <NA>
## Q121700.fctr -1.373009e-02 0 1.373009e-02 Q121699.fctr
## Q106042.fctr -1.533181e-02 0 1.533181e-02 <NA>
## Q116441.fctr -1.548317e-02 0 1.548317e-02 <NA>
## YOB.Age.dff -1.579002e-02 0 1.579002e-02 <NA>
## Q119650.fctr -1.650551e-02 0 1.650551e-02 <NA>
## Q120978.fctr -1.708157e-02 0 1.708157e-02 <NA>
## Income.fctr -1.805407e-02 0 1.805407e-02 <NA>
## Q99716.fctr -1.819015e-02 0 1.819015e-02 <NA>
## Q102687.fctr -1.939956e-02 0 1.939956e-02 <NA>
## Q107491.fctr -1.953297e-02 0 1.953297e-02 <NA>
## Q100010.fctr -2.019365e-02 0 2.019365e-02 <NA>
## Q112270.fctr -2.229876e-02 0 2.229876e-02 <NA>
## Q123464.fctr -2.303014e-02 0 2.303014e-02 <NA>
## Q104996.fctr -2.394016e-02 0 2.394016e-02 <NA>
## Q116797.fctr -2.401541e-02 0 2.401541e-02 <NA>
## Q116601.fctr -2.424136e-02 0 2.424136e-02 <NA>
## Q116953.fctr -2.434193e-02 0 2.434193e-02 <NA>
## Q110740.fctr -2.599180e-02 0 2.599180e-02 <NA>
## Q103293.fctr -2.609312e-02 0 2.609312e-02 <NA>
## Q122120.fctr -2.635370e-02 0 2.635370e-02 <NA>
## Q108950.fctr -2.796314e-02 0 2.796314e-02 <NA>
## Q100680.fctr -2.839756e-02 0 2.839756e-02 <NA>
## Q122769.fctr -2.867161e-02 0 2.867161e-02 <NA>
## Q106993.fctr -2.914206e-02 0 2.914206e-02 <NA>
## Q111848.fctr -3.061679e-02 0 3.061679e-02 <NA>
## Q121011.fctr -3.123022e-02 0 3.123022e-02 <NA>
## Q115195.fctr -3.162016e-02 0 3.162016e-02 <NA>
## Q120650.fctr -3.168805e-02 0 3.168805e-02 <NA>
## Q96024.fctr -3.182566e-02 0 3.182566e-02 <NA>
## Q112512.fctr -3.209601e-02 0 3.209601e-02 <NA>
## Q118233.fctr -3.272307e-02 0 3.272307e-02 <NA>
## Q116448.fctr -3.325689e-02 0 3.325689e-02 <NA>
## Q106389.fctr -3.366341e-02 0 3.366341e-02 <NA>
## Q118237.fctr -3.406424e-02 0 3.406424e-02 <NA>
## Q124742.fctr -3.410578e-02 0 3.410578e-02 <NA>
## Q111220.fctr -3.435982e-02 0 3.435982e-02 <NA>
## Q117186.fctr -3.488234e-02 0 3.488234e-02 <NA>
## Q106272.fctr -3.593469e-02 0 3.593469e-02 <NA>
## Q98059.fctr -3.688543e-02 0 3.688543e-02 <NA>
## Q120379.fctr -3.777954e-02 0 3.777954e-02 <NA>
## Q105840.fctr -3.782042e-02 0 3.782042e-02 <NA>
## Q114961.fctr -3.846716e-02 0 3.846716e-02 <NA>
## Q98578.fctr -3.905193e-02 0 3.905193e-02 <NA>
## Q122770.fctr -3.924562e-02 0 3.924562e-02 <NA>
## Q106997.fctr -3.925127e-02 0 3.925127e-02 <NA>
## YOB.Age.fctr -4.299538e-02 0 4.299538e-02 <NA>
## Q98078.fctr -4.318029e-02 0 4.318029e-02 <NA>
## Q100689.fctr -4.610720e-02 0 4.610720e-02 <NA>
## Q119851.fctr -4.784957e-02 0 4.784957e-02 <NA>
## Q112478.fctr -4.791303e-02 0 4.791303e-02 <NA>
## Q118892.fctr -4.849461e-02 0 4.849461e-02 <NA>
## Q116197.fctr -4.953483e-02 0 4.953483e-02 <NA>
## Q113992.fctr -5.060894e-02 0 5.060894e-02 <NA>
## Q123621.fctr -5.140758e-02 0 5.140758e-02 <NA>
## Q120012.fctr -5.301143e-02 0 5.301143e-02 <NA>
## Q115390.fctr -5.425424e-02 0 5.425424e-02 <NA>
## Q121699.fctr -6.186040e-02 0 6.186040e-02 <NA>
## Q114517.fctr -6.233932e-02 0 6.233932e-02 <NA>
## Q124122.fctr -6.976947e-02 0 6.976947e-02 <NA>
## Q114386.fctr -7.613008e-02 0 7.613008e-02 <NA>
## Q114152.fctr -7.783674e-02 0 7.783674e-02 <NA>
## Q109244.fctr NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## YOB 1.055556 6.8108108 FALSE FALSE FALSE
## .clusterid 1.804082 0.6486486 FALSE FALSE FALSE
## .clusterid.fctr 1.804082 0.6486486 FALSE FALSE FALSE
## Gender.fctr 1.531680 0.3243243 FALSE FALSE FALSE
## Q108754.fctr 1.830986 0.3243243 FALSE FALSE FALSE
## Q108856.fctr 2.574074 0.3243243 FALSE FALSE FALSE
## Q120014.fctr 1.266447 0.3243243 FALSE FALSE FALSE
## Q115611.fctr 3.698795 0.3243243 FALSE FALSE FALSE
## Q102906.fctr 1.571429 0.3243243 FALSE FALSE FALSE
## Q101596.fctr 2.316514 0.3243243 FALSE FALSE FALSE
## .pos 1.000000 100.0000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.0000000 FALSE FALSE FALSE
## Q120194.fctr 1.694118 0.3243243 FALSE FALSE FALSE
## Hhold.fctr 1.820961 0.7567568 FALSE FALSE FALSE
## Q99480.fctr 2.540284 0.3243243 FALSE FALSE FALSE
## Q108343.fctr 1.331361 0.3243243 FALSE FALSE FALSE
## Q108617.fctr 5.685950 0.3243243 FALSE FALSE FALSE
## Q108855.fctr 1.422713 0.3243243 FALSE FALSE FALSE
## Q109367.fctr 1.912458 0.3243243 FALSE FALSE FALSE
## Q117193.fctr 1.404844 0.3243243 FALSE FALSE FALSE
## Q99982.fctr 1.519298 0.3243243 FALSE FALSE TRUE
## Q114748.fctr 1.256716 0.3243243 FALSE FALSE TRUE
## Q111580.fctr 1.646429 0.3243243 FALSE FALSE TRUE
## Q98197.fctr 2.581731 0.3243243 FALSE FALSE TRUE
## Q101163.fctr 1.098802 0.3243243 FALSE FALSE TRUE
## Q102289.fctr 2.734694 0.3243243 FALSE FALSE TRUE
## Q116881.fctr 2.200873 0.3243243 FALSE FALSE TRUE
## Q101162.fctr 1.716912 0.3243243 FALSE FALSE TRUE
## Q102674.fctr 1.614286 0.3243243 FALSE FALSE TRUE
## Q102089.fctr 1.779851 0.3243243 FALSE FALSE TRUE
## Q118232.fctr 1.185065 0.3243243 FALSE FALSE TRUE
## Q118117.fctr 1.736059 0.3243243 FALSE FALSE TRUE
## Q99581.fctr 3.551913 0.3243243 FALSE FALSE TRUE
## Q108342.fctr 1.656667 0.3243243 FALSE FALSE TRUE
## Q113584.fctr 1.008380 0.3243243 FALSE FALSE TRUE
## Edn.fctr 1.519737 0.8648649 FALSE FALSE TRUE
## Q113181.fctr 2.378995 0.3243243 FALSE FALSE TRUE
## Q115899.fctr 1.135542 0.3243243 FALSE FALSE TRUE
## Q122771.fctr 2.936275 0.3243243 FALSE FALSE TRUE
## Q106388.fctr 2.675000 0.3243243 FALSE FALSE TRUE
## Q113583.fctr 2.387850 0.3243243 FALSE FALSE TRUE
## Q119334.fctr 1.069767 0.3243243 FALSE FALSE TRUE
## Q105655.fctr 1.210826 0.3243243 FALSE FALSE TRUE
## Q115777.fctr 1.436426 0.3243243 FALSE FALSE TRUE
## Q98869.fctr 2.187500 0.3243243 FALSE FALSE TRUE
## Q115602.fctr 2.958974 0.3243243 FALSE FALSE TRUE
## Q107869.fctr 1.021220 0.3243243 FALSE FALSE TRUE
## .rnorm 1.000000 100.0000000 FALSE FALSE FALSE
## Q120472.fctr 1.631579 0.3243243 FALSE FALSE FALSE
## Q100562.fctr 3.216931 0.3243243 FALSE FALSE FALSE
## Q115610.fctr 3.240642 0.3243243 FALSE FALSE FALSE
## Q121700.fctr 3.472826 0.3243243 FALSE FALSE FALSE
## Q106042.fctr 1.235650 0.3243243 FALSE FALSE FALSE
## Q116441.fctr 2.041667 0.3243243 FALSE FALSE FALSE
## YOB.Age.dff 1.027586 1.7297297 FALSE FALSE FALSE
## Q119650.fctr 2.458150 0.3243243 FALSE FALSE FALSE
## Q120978.fctr 1.331148 0.3243243 FALSE FALSE FALSE
## Income.fctr 1.056738 0.7567568 FALSE FALSE FALSE
## Q99716.fctr 3.418478 0.3243243 FALSE FALSE FALSE
## Q102687.fctr 1.125698 0.3243243 FALSE FALSE FALSE
## Q107491.fctr 4.744966 0.3243243 FALSE FALSE FALSE
## Q100010.fctr 3.204420 0.3243243 FALSE FALSE FALSE
## Q112270.fctr 1.675373 0.3243243 FALSE FALSE FALSE
## Q123464.fctr 2.484127 0.3243243 FALSE FALSE FALSE
## Q104996.fctr 1.071038 0.3243243 FALSE FALSE FALSE
## Q116797.fctr 1.715356 0.3243243 FALSE FALSE FALSE
## Q116601.fctr 3.411765 0.3243243 FALSE FALSE FALSE
## Q116953.fctr 2.000000 0.3243243 FALSE FALSE FALSE
## Q110740.fctr 1.245665 0.3243243 FALSE FALSE FALSE
## Q103293.fctr 1.102778 0.3243243 FALSE FALSE FALSE
## Q122120.fctr 2.651961 0.3243243 FALSE FALSE FALSE
## Q108950.fctr 2.028470 0.3243243 FALSE FALSE FALSE
## Q100680.fctr 2.336323 0.3243243 FALSE FALSE FALSE
## Q122769.fctr 1.627376 0.3243243 FALSE FALSE FALSE
## Q106993.fctr 3.842767 0.3243243 FALSE FALSE FALSE
## Q111848.fctr 2.304721 0.3243243 FALSE FALSE FALSE
## Q121011.fctr 1.541958 0.3243243 FALSE FALSE FALSE
## Q115195.fctr 2.173913 0.3243243 FALSE FALSE FALSE
## Q120650.fctr 3.084507 0.3243243 FALSE FALSE FALSE
## Q96024.fctr 1.284810 0.3243243 FALSE FALSE FALSE
## Q112512.fctr 3.346154 0.3243243 FALSE FALSE FALSE
## Q118233.fctr 2.375000 0.3243243 FALSE FALSE FALSE
## Q116448.fctr 1.019337 0.3243243 FALSE FALSE FALSE
## Q106389.fctr 1.122807 0.3243243 FALSE FALSE FALSE
## Q118237.fctr 1.005682 0.3243243 FALSE FALSE FALSE
## Q124742.fctr 1.712230 0.3243243 FALSE FALSE FALSE
## Q111220.fctr 2.500000 0.3243243 FALSE FALSE FALSE
## Q117186.fctr 1.983051 0.3243243 FALSE FALSE FALSE
## Q106272.fctr 1.921875 0.3243243 FALSE FALSE FALSE
## Q98059.fctr 4.217391 0.3243243 FALSE FALSE FALSE
## Q120379.fctr 1.279743 0.3243243 FALSE FALSE FALSE
## Q105840.fctr 1.295031 0.3243243 FALSE FALSE FALSE
## Q114961.fctr 1.095930 0.3243243 FALSE FALSE FALSE
## Q98578.fctr 1.853175 0.3243243 FALSE FALSE FALSE
## Q122770.fctr 1.355049 0.3243243 FALSE FALSE FALSE
## Q106997.fctr 1.095109 0.3243243 FALSE FALSE FALSE
## YOB.Age.fctr 1.100719 0.9729730 FALSE FALSE FALSE
## Q98078.fctr 1.051576 0.3243243 FALSE FALSE FALSE
## Q100689.fctr 1.865672 0.3243243 FALSE FALSE FALSE
## Q119851.fctr 1.059322 0.3243243 FALSE FALSE FALSE
## Q112478.fctr 1.733083 0.3243243 FALSE FALSE FALSE
## Q118892.fctr 2.668317 0.3243243 FALSE FALSE FALSE
## Q116197.fctr 1.708333 0.3243243 FALSE FALSE FALSE
## Q113992.fctr 2.575472 0.3243243 FALSE FALSE FALSE
## Q123621.fctr 1.002985 0.3243243 FALSE FALSE FALSE
## Q120012.fctr 1.019943 0.3243243 FALSE FALSE FALSE
## Q115390.fctr 1.543478 0.3243243 FALSE FALSE FALSE
## Q121699.fctr 3.090909 0.3243243 FALSE FALSE FALSE
## Q114517.fctr 2.334821 0.3243243 FALSE FALSE FALSE
## Q124122.fctr 1.375912 0.3243243 FALSE FALSE FALSE
## Q114386.fctr 1.211310 0.3243243 FALSE FALSE FALSE
## Q114152.fctr 2.319444 0.3243243 FALSE FALSE FALSE
## Q109244.fctr 0.000000 0.1081081 TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr NA 0 NA <NA> 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 0.1081081 TRUE TRUE NA## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in : "
## YOB Party.fctr
## 48 223
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 49
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 9 163 59 136
## Party Q124742 Q124122 Q123464
## NA 589 337 309
## Q123621 Q122769 Q122770 Q122771
## 312 279 245 247
## Q122120 Q121699 Q121700 Q120978
## 248 224 222 257
## Q121011 Q120379 Q120650 Q120472
## 238 269 261 280
## Q120194 Q120012 Q120014 Q119334
## 292 265 289 257
## Q119851 Q119650 Q118892 Q118117
## 245 274 227 228
## Q118232 Q118233 Q118237 Q117186
## 311 268 264 283
## Q117193 Q116797 Q116881 Q116953
## 276 242 276 279
## Q116601 Q116441 Q116448 Q116197
## 225 239 243 248
## Q115602 Q115777 Q115610 Q115611
## 236 264 230 202
## Q115899 Q115390 Q114961 Q114748
## 260 279 244 207
## Q115195 Q114517 Q114386 Q113992
## 242 216 214 206
## Q114152 Q113583 Q113584 Q113181
## 261 235 241 217
## Q112478 Q112512 Q112270 Q111848
## 229 212 249 179
## Q111580 Q111220 Q110740 Q109367
## 219 191 173 73
## Q108950 Q109244 Q108855 Q108617
## 91 0 191 143
## Q108856 Q108754 Q108342 Q108343
## 183 148 157 162
## Q107869 Q107491 Q106993 Q106997
## 196 176 187 193
## Q106272 Q106388 Q106389 Q106042
## 212 228 236 218
## Q105840 Q105655 Q104996 Q103293
## 222 177 201 201
## Q102906 Q102674 Q102687 Q102289
## 231 237 199 227
## Q102089 Q101162 Q101163 Q101596
## 222 224 271 243
## Q100689 Q100680 Q100562 Q99982
## 185 216 226 243
## Q100010 Q99716 Q99581 Q99480
## 210 219 214 206
## Q98869 Q98578 Q98059 Q98078
## 254 252 195 251
## Q98197 Q96024 .lcn
## 224 243 223## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.02272035 TRUE 0.02272035 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 3 select.features 3 0 0 214.858 217.786
## 4 fit.models 4 0 0 217.786 NA
## elapsed
## 3 2.928
## 4 NAStep 4.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 218.316 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 218.316 218.351
## 2 fit.models_0_MFO 1 1 myMFO_classfr 218.352 NA
## elapsed
## 1 0.035
## 2 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.507000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.8060109 0.1939891
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 1.027000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 1.031000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.8060109 0
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.7875648 0
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 8.159000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.499
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8060109
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7754723 0.8340591 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "myfit_mdl: exit: 8.264000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 218.352
## 3 fit.models_0_Random 1 2 myrandom_classfr 226.622
## end elapsed
## 2 226.621 8.269
## 3 NA NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.406000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.678000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.680000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 7.571000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.268 0.002 0.5244092
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8305085 0.2183099 0.4867868 0.85
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060109 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.4922978 0.7894737
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.195122 0.5012035 0.85 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB
## 1 0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.803000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 226.622 234.437 7.815
## 4 234.437 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.695000 secs"## Loading required package: glmnet## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.000641 on full training set
## [1] "myfit_mdl: train complete: 1.498000 secs"
## alpha lambda
## 1 0.1 0.0006408924
## Length Class Mode
## a0 48 -none- numeric
## beta 192 dgCMatrix S4
## df 48 -none- numeric
## dim 2 -none- numeric
## lambda 48 -none- numeric
## dev.ratio 48 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Q114152.fctrNo Q114152.fctrYes
## -1.13166150 0.01441013 -0.47004128
## Q114386.fctrMysterious Q114386.fctrTMI
## -0.30122736 -0.23102023
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Q114152.fctrNo"
## [3] "Q114152.fctrYes" "Q114386.fctrMysterious"
## [5] "Q114386.fctrTMI"
## [1] "myfit_mdl: train diagnostics complete: 1.606000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 8.343000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q114152.fctr,Q114386.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.792 0.015 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5625269 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060109 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.5478979 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 8.391000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.706000 secs"## Loading required package: rpart## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.093000 secs"
## cp
## 1 0## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 732
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 732 observations
## predicted class=D expected loss=0.1939891 P(node) =1
## class counts: 590 142
## probabilities: 0.806 0.194
##
## n= 732
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 732 142 D (0.8060109 0.1939891) *
## [1] "myfit_mdl: train diagnostics complete: 2.297000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.8060109 0
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.7875648 0
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 8.811000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q114152.fctr,Q114386.fctr 1
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.382 0.009 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060121 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.5 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.001793698 0
## [1] "myfit_mdl: exit: 8.868000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 234.437 251.744 17.307
## 5 251.745 NA NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0211 on full training set
## [1] "myfit_mdl: train complete: 2.503000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 56 -none- numeric
## beta 560 dgCMatrix S4
## df 56 -none- numeric
## dim 2 -none- numeric
## lambda 56 -none- numeric
## dev.ratio 56 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Q114152.fctrNA:Q121699.fctrNo
## -1.3660730 0.8520755
## Q114152.fctrNA:Q121699.fctrYes Q114152.fctrYes:Q121699.fctrYes
## -0.1969547 -0.6620578
## [1] "max lambda < lambdaOpt:"
## (Intercept) Q114152.fctrNA:Q121699.fctrNo
## -1.362457755 0.874180212
## Q114152.fctrYes:Q121699.fctrNo Q114152.fctrNA:Q121699.fctrYes
## 0.009467681 -0.225224906
## Q114152.fctrYes:Q121699.fctrYes
## -0.693412776
## [1] "myfit_mdl: train diagnostics complete: 3.140000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 9.872000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats max.nTuningRuns
## 1 Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr 20
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.8 0.017 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5972487 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060121 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.4963094 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.001881246 0
## [1] "myfit_mdl: exit: 9.927000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 251.745 261.684 9.939
## 6 261.685 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.995000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0184 on full training set
## [1] "myfit_mdl: train complete: 20.125000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 76 -none- numeric
## beta 20672 dgCMatrix S4
## df 76 -none- numeric
## dim 2 -none- numeric
## lambda 76 -none- numeric
## dev.ratio 76 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 272 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.304392159 -0.439537760
## Edn.fctr^5 Hhold.fctrPKn
## 0.289815605 -0.102733376
## Income.fctr.Q Q100562.fctrNo
## 0.035140876 0.192701637
## Q100680.fctrNo Q101596.fctrNo
## 0.051459986 0.060317747
## Q106042.fctrNo Q106272.fctrNo
## 0.031547125 0.045188938
## Q112478.fctrNo Q114152.fctrYes
## 0.172609276 -0.237181891
## Q114517.fctrYes Q115390.fctrNo
## -0.073991003 0.050676283
## Q115610.fctrNo Q116197.fctrA.M.
## 0.213999385 0.182705143
## Q116953.fctrNo Q117186.fctrCool headed
## 0.147642351 0.054401980
## Q117193.fctrStandard hours Q118232.fctrId
## 0.063624451 -0.356032987
## Q119851.fctrNo Q120012.fctrYes
## 0.067159669 -0.006650210
## Q120194.fctrStudy first Q120194.fctrTry first
## -0.255962047 0.091674315
## Q120379.fctrNo Q122770.fctrYes
## 0.003427111 -0.010960486
## Q124742.fctrNo Q98197.fctrNo
## -0.061449075 -0.418430139
## Q98578.fctrYes YOB.Age.fctr^7
## -0.148040499 0.083991528
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrN:.clusterid.fctr3
## 0.178777476 0.463131099
## YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.053056489
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.334147378 -0.488255054
## Edn.fctr^5 Hhold.fctrPKn
## 0.297656790 -0.130366919
## Hhold.fctrPKy Income.fctr.Q
## -0.050236070 0.078563664
## Q100562.fctrNo Q100680.fctrNo
## 0.233839610 0.069212044
## Q101596.fctrNo Q106042.fctrNo
## 0.083840630 0.053431101
## Q106272.fctrNo Q111220.fctrNo
## 0.067618611 0.015344329
## Q112478.fctrNo Q114152.fctrYes
## 0.198562787 -0.269581628
## Q114517.fctrYes Q115390.fctrNo
## -0.110498672 0.076713084
## Q115610.fctrNo Q115611.fctrYes
## 0.245686495 0.006136154
## Q116197.fctrA.M. Q116953.fctrNo
## 0.213931597 0.165130852
## Q117186.fctrCool headed Q117193.fctrStandard hours
## 0.076437866 0.079222420
## Q118232.fctrId Q119851.fctrNo
## -0.392152299 0.096779981
## Q120012.fctrYes Q120194.fctrStudy first
## -0.017106255 -0.290853414
## Q120194.fctrTry first Q120379.fctrNo
## 0.088592843 0.030397334
## Q122770.fctrYes Q124742.fctrNo
## -0.033410864 -0.090760407
## Q98197.fctrNo Q98578.fctrYes
## -0.471332186 -0.181444807
## YOB.Age.fctr^7 Hhold.fctrN:.clusterid.fctr2
## 0.129503267 -0.113206539
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrN:.clusterid.fctr3
## 0.195739632 0.596454864
## YOB.Age.fctr(15,20]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.059943113 -0.001056719
## [1] "myfit_mdl: train diagnostics complete: 20.804000 secs"
## Prediction
## Reference D R
## D 572 18
## R 114 28
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.196721e-01 2.242276e-01 7.898676e-01 8.468728e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 1.878017e-01 1.354405e-16
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 30.285000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 19.028 3.643
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.758773
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.2978723 0.8032761
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7898676 0.8468728 -0.005332598
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5547176
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003689189 0.00799892
## [1] "myfit_mdl: exit: 30.506000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 261.685 292.222
## 7 fit.models_0_end 1 6 teardown 292.222 NA
## elapsed
## 6 30.537
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 4 0 0 217.786 292.236 74.45
## 5 fit.models 4 1 1 292.236 NA NAif (!is.null(glbChunks$first) && (glbChunks$first == "fit.models_1") &&
(is.null(knitr::opts_current$get(name = 'label')))) # not knitting
myloadChunk(glbChunks$inpFilePathName,
keepSpec = c("glbMdlFamilies","glbMdlSequential","glbMdlPreprocMethods",
"glbMdlTuneParams","glbMdlSltId","glbMdlEnsemble"),
dropSpec = c(NULL))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 296.713 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 296.713 296.726
## 2 fit.models_1_All.X 1 1 setup 296.727 NA
## elapsed
## 1 0.013
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 296.727 296.734
## 3 fit.models_1_All.X 1 2 glmnet 296.735 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.755000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.06 on full training set
## [1] "myfit_mdl: train complete: 19.074000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 95 -none- numeric
## beta 26030 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 274 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q Edn.fctr^5
## -1.34424652 -0.02766377 0.10286262
## Q118232.fctrId Q120194.fctrStudy first Q98197.fctrNo
## -0.04101728 -0.05953352 -0.07090710
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.31117916 -0.08057063
## Edn.fctr^5 Q118232.fctrId
## 0.14039872 -0.07913025
## Q120194.fctrStudy first Q121699.fctrNo
## -0.09265405 0.03169800
## Q98197.fctrNo Hhold.fctrSKn:.clusterid.fctr2
## -0.10407511 0.03857097
## [1] "myfit_mdl: train diagnostics complete: 19.734000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 28.947000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 18.247 4.045
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.6674564
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8060121
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7754723 0.8340591 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5698813
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.001881246 0
## [1] "myfit_mdl: exit: 29.165000 secs"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 296.735 325.914
## 4 fit.models_1_preProc 1 3 preProc 325.915 NA
## elapsed
## 3 29.179
## 4 NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.7875648 0.5698813
## Low.cor.X##rcv#glmnet 0.7875648 0.5547176
## Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## Random###myrandom_classfr 0.7875648 0.5012035
## MFO###myMFO_classfr 0.7875648 0.5000000
## Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.5000000 18.247
## Low.cor.X##rcv#glmnet 0.5000000 19.028
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.792
## Random###myrandom_classfr 0.4922978 0.268
## MFO###myMFO_classfr 0.5000000 0.499
## Max.cor.Y##rcv#rpart 0.5000000 1.382
## Interact.High.cor.Y##rcv#glmnet 0.5000000 1.800
## max.Accuracy.fit
## All.X##rcv#glmnet 0.8060121
## Low.cor.X##rcv#glmnet 0.8032761
## Max.cor.Y.rcv.1X1###glmnet 0.8060109
## Random###myrandom_classfr 0.8060109
## MFO###myMFO_classfr 0.8060109
## Max.cor.Y##rcv#rpart 0.8060121
## Interact.High.cor.Y##rcv#glmnet 0.8060121## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 325.915 327.735
## 5 fit.models_1_end 1 4 teardown 327.736 NA
## elapsed
## 4 1.82
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 5 fit.models 4 1 1 292.236 327.746 35.51
## 6 fit.models 4 2 2 327.746 NA NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 328.881 NA NA## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Warning: Stacking not well defined when ymin != 0## Warning: Removed 4 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Stacking not well defined when ymin != 0
## Warning: Removed 4 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 7 All.X##rcv#glmnet 0.7875648 0.5698813
## 6 Low.cor.X##rcv#glmnet 0.7875648 0.5547176
## 3 Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## 2 Random###myrandom_classfr 0.7875648 0.5012035
## 1 MFO###myMFO_classfr 0.7875648 0.5000000
## 4 Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## 5 Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7 0.5000000 18.247 0.8060121
## 6 0.5000000 19.028 0.8032761
## 3 0.5000000 0.792 0.8060109
## 2 0.4922978 0.268 0.8060109
## 1 0.5000000 0.499 0.8060109
## 4 0.5000000 1.382 0.8060121
## 5 0.5000000 1.800 0.8060121
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 7 0.50 0.50
## 6 0.30 0.50
## 3 0.50 0.50
## 2 0.85 0.85
## 1 0.50 0.50
## 4 0.50 0.50
## 5 0.50 0.50## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7f8b67c05df0>## [1] "Best model id: All.X##rcv#glmnet"## glmnet
##
## 732 samples
## 108 predictors
## 2 classes: 'D', 'R'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times)
## Summary of sample sizes: 488, 488, 488, 489, 488, 487, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.325 0.001034113 0.6816965 0.0061195860
## 0.325 0.004799925 0.7131304 0.0204687692
## 0.325 0.022279280 0.7745893 0.0129944722
## 0.325 0.040000000 0.8019137 0.0062034440
## 0.325 0.060000000 0.8050994 -0.0018017059
## 0.550 0.001034113 0.6785164 -0.0014737461
## 0.550 0.004799925 0.7208624 0.0186156194
## 0.550 0.022279280 0.7937132 0.0066025599
## 0.550 0.040000000 0.8050994 -0.0018017059
## 0.550 0.060000000 0.8060121 0.0000000000
## 0.775 0.001034113 0.6780610 -0.0005383534
## 0.775 0.004799925 0.7331650 0.0252004353
## 0.775 0.022279280 0.8023653 -0.0014472048
## 0.775 0.040000000 0.8060121 0.0000000000
## 0.775 0.060000000 0.8060121 0.0000000000
## 0.900 0.001034113 0.6785127 -0.0014430410
## 0.900 0.004799925 0.7368118 0.0266229561
## 0.900 0.022279280 0.8037315 -0.0044565272
## 0.900 0.040000000 0.8060121 0.0000000000
## 0.900 0.060000000 0.8060121 0.0000000000
## 1.000 0.001034113 0.6789681 0.0040497687
## 1.000 0.004799925 0.7404455 0.0236437888
## 1.000 0.022279280 0.8046441 -0.0027006941
## 1.000 0.040000000 0.8060121 0.0000000000
## 1.000 0.060000000 0.8060121 0.0000000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.55 and lambda = 0.06.## [1] "All.X##rcv#glmnet fit prediction diagnostics:"## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"## All.X..rcv.glmnet.imp imp
## Edn.fctr^5 100.00000 100.00000
## Q98197.fctrNo 73.29298 73.29298
## Q120194.fctrStudy first 64.68834 64.68834
## Q118232.fctrId 53.71026 53.71026
## Edn.fctr.Q 52.48374 52.48374
## Hhold.fctrSKn:.clusterid.fctr2 23.05489 23.05489
## Q121699.fctrNo 18.94674 18.94674
## .rnorm 0.00000 0.00000
## Edn.fctr.L 0.00000 0.00000
## Edn.fctr.C 0.00000 0.00000
## Edn.fctr^4 0.00000 0.00000
## Edn.fctr^6 0.00000 0.00000
## Edn.fctr^7 0.00000 0.00000
## Gender.fctrF 0.00000 0.00000
## Gender.fctrM 0.00000 0.00000
## Hhold.fctrMKn 0.00000 0.00000
## Hhold.fctrMKy 0.00000 0.00000
## Hhold.fctrPKn 0.00000 0.00000
## Hhold.fctrPKy 0.00000 0.00000
## Hhold.fctrSKn 0.00000 0.00000
## Hhold.fctrSKy 0.00000 0.00000
## Income.fctr.L 0.00000 0.00000
## Income.fctr.Q 0.00000 0.00000
## Income.fctr.C 0.00000 0.00000
## Income.fctr^4 0.00000 0.00000
## Income.fctr^5 0.00000 0.00000
## Income.fctr^6 0.00000 0.00000
## Q100010.fctrNo 0.00000 0.00000
## Q100010.fctrYes 0.00000 0.00000
## Q100562.fctrNo 0.00000 0.00000
## Q100562.fctrYes 0.00000 0.00000
## Q100680.fctrNo 0.00000 0.00000
## Q100680.fctrYes 0.00000 0.00000
## Q100689.fctrNo 0.00000 0.00000
## Q100689.fctrYes 0.00000 0.00000
## Q101162.fctrOptimist 0.00000 0.00000
## Q101162.fctrPessimist 0.00000 0.00000
## Q101163.fctrDad 0.00000 0.00000
## Q101163.fctrMom 0.00000 0.00000
## Q101596.fctrNo 0.00000 0.00000
## Q101596.fctrYes 0.00000 0.00000
## Q102089.fctrOwn 0.00000 0.00000
## Q102089.fctrRent 0.00000 0.00000
## Q102289.fctrNo 0.00000 0.00000
## Q102289.fctrYes 0.00000 0.00000
## Q102674.fctrNo 0.00000 0.00000
## Q102674.fctrYes 0.00000 0.00000
## Q102687.fctrNo 0.00000 0.00000
## Q102687.fctrYes 0.00000 0.00000
## Q102906.fctrNo 0.00000 0.00000
## Q102906.fctrYes 0.00000 0.00000
## Q103293.fctrNo 0.00000 0.00000
## Q103293.fctrYes 0.00000 0.00000
## Q104996.fctrNo 0.00000 0.00000
## Q104996.fctrYes 0.00000 0.00000
## Q105655.fctrNo 0.00000 0.00000
## Q105655.fctrYes 0.00000 0.00000
## Q105840.fctrNo 0.00000 0.00000
## Q105840.fctrYes 0.00000 0.00000
## Q106042.fctrNo 0.00000 0.00000
## Q106042.fctrYes 0.00000 0.00000
## Q106272.fctrNo 0.00000 0.00000
## Q106272.fctrYes 0.00000 0.00000
## Q106388.fctrNo 0.00000 0.00000
## Q106388.fctrYes 0.00000 0.00000
## Q106389.fctrNo 0.00000 0.00000
## Q106389.fctrYes 0.00000 0.00000
## Q106993.fctrNo 0.00000 0.00000
## Q106993.fctrYes 0.00000 0.00000
## Q106997.fctrGr 0.00000 0.00000
## Q106997.fctrYy 0.00000 0.00000
## Q107491.fctrNo 0.00000 0.00000
## Q107491.fctrYes 0.00000 0.00000
## Q107869.fctrNo 0.00000 0.00000
## Q107869.fctrYes 0.00000 0.00000
## Q108342.fctrIn-person 0.00000 0.00000
## Q108342.fctrOnline 0.00000 0.00000
## Q108343.fctrNo 0.00000 0.00000
## Q108343.fctrYes 0.00000 0.00000
## Q108617.fctrNo 0.00000 0.00000
## Q108617.fctrYes 0.00000 0.00000
## Q108754.fctrNo 0.00000 0.00000
## Q108754.fctrYes 0.00000 0.00000
## Q108855.fctrUmm... 0.00000 0.00000
## Q108855.fctrYes! 0.00000 0.00000
## Q108856.fctrSocialize 0.00000 0.00000
## Q108856.fctrSpace 0.00000 0.00000
## Q108950.fctrCautious 0.00000 0.00000
## Q108950.fctrRisk-friendly 0.00000 0.00000
## Q109367.fctrNo 0.00000 0.00000
## Q109367.fctrYes 0.00000 0.00000
## Q110740.fctrMac 0.00000 0.00000
## Q110740.fctrPC 0.00000 0.00000
## Q111220.fctrNo 0.00000 0.00000
## Q111220.fctrYes 0.00000 0.00000
## Q111580.fctrDemanding 0.00000 0.00000
## Q111580.fctrSupportive 0.00000 0.00000
## Q111848.fctrNo 0.00000 0.00000
## Q111848.fctrYes 0.00000 0.00000
## Q112270.fctrNo 0.00000 0.00000
## Q112270.fctrYes 0.00000 0.00000
## Q112478.fctrNo 0.00000 0.00000
## Q112478.fctrYes 0.00000 0.00000
## Q112512.fctrNo 0.00000 0.00000
## Q112512.fctrYes 0.00000 0.00000
## Q113181.fctrNo 0.00000 0.00000
## Q113181.fctrYes 0.00000 0.00000
## Q113583.fctrTalk 0.00000 0.00000
## Q113583.fctrTunes 0.00000 0.00000
## Q113584.fctrPeople 0.00000 0.00000
## Q113584.fctrTechnology 0.00000 0.00000
## Q113992.fctrNo 0.00000 0.00000
## Q113992.fctrYes 0.00000 0.00000
## Q114152.fctrNo 0.00000 0.00000
## Q114152.fctrYes 0.00000 0.00000
## Q114386.fctrMysterious 0.00000 0.00000
## Q114386.fctrTMI 0.00000 0.00000
## Q114517.fctrNo 0.00000 0.00000
## Q114517.fctrYes 0.00000 0.00000
## Q114748.fctrNo 0.00000 0.00000
## Q114748.fctrYes 0.00000 0.00000
## Q114961.fctrNo 0.00000 0.00000
## Q114961.fctrYes 0.00000 0.00000
## Q115195.fctrNo 0.00000 0.00000
## Q115195.fctrYes 0.00000 0.00000
## Q115390.fctrNo 0.00000 0.00000
## Q115390.fctrYes 0.00000 0.00000
## Q115602.fctrNo 0.00000 0.00000
## Q115602.fctrYes 0.00000 0.00000
## Q115610.fctrNo 0.00000 0.00000
## Q115610.fctrYes 0.00000 0.00000
## Q115611.fctrNo 0.00000 0.00000
## Q115611.fctrYes 0.00000 0.00000
## Q115777.fctrEnd 0.00000 0.00000
## Q115777.fctrStart 0.00000 0.00000
## Q115899.fctrCs 0.00000 0.00000
## Q115899.fctrMe 0.00000 0.00000
## Q116197.fctrA.M. 0.00000 0.00000
## Q116197.fctrP.M. 0.00000 0.00000
## Q116441.fctrNo 0.00000 0.00000
## Q116441.fctrYes 0.00000 0.00000
## Q116448.fctrNo 0.00000 0.00000
## Q116448.fctrYes 0.00000 0.00000
## Q116601.fctrNo 0.00000 0.00000
## Q116601.fctrYes 0.00000 0.00000
## Q116797.fctrNo 0.00000 0.00000
## Q116797.fctrYes 0.00000 0.00000
## Q116881.fctrHappy 0.00000 0.00000
## Q116881.fctrRight 0.00000 0.00000
## Q116953.fctrNo 0.00000 0.00000
## Q116953.fctrYes 0.00000 0.00000
## Q117186.fctrCool headed 0.00000 0.00000
## Q117186.fctrHot headed 0.00000 0.00000
## Q117193.fctrOdd hours 0.00000 0.00000
## Q117193.fctrStandard hours 0.00000 0.00000
## Q118117.fctrNo 0.00000 0.00000
## Q118117.fctrYes 0.00000 0.00000
## Q118232.fctrPr 0.00000 0.00000
## Q118233.fctrNo 0.00000 0.00000
## Q118233.fctrYes 0.00000 0.00000
## Q118237.fctrNo 0.00000 0.00000
## Q118237.fctrYes 0.00000 0.00000
## Q118892.fctrNo 0.00000 0.00000
## Q118892.fctrYes 0.00000 0.00000
## Q119334.fctrNo 0.00000 0.00000
## Q119334.fctrYes 0.00000 0.00000
## Q119650.fctrGiving 0.00000 0.00000
## Q119650.fctrReceiving 0.00000 0.00000
## Q119851.fctrNo 0.00000 0.00000
## Q119851.fctrYes 0.00000 0.00000
## Q120012.fctrNo 0.00000 0.00000
## Q120012.fctrYes 0.00000 0.00000
## Q120014.fctrNo 0.00000 0.00000
## Q120014.fctrYes 0.00000 0.00000
## Q120194.fctrTry first 0.00000 0.00000
## Q120379.fctrNo 0.00000 0.00000
## Q120379.fctrYes 0.00000 0.00000
## Q120472.fctrArt 0.00000 0.00000
## Q120472.fctrScience 0.00000 0.00000
## Q120650.fctrNo 0.00000 0.00000
## Q120650.fctrYes 0.00000 0.00000
## Q120978.fctrNo 0.00000 0.00000
## Q120978.fctrYes 0.00000 0.00000
## Q121011.fctrNo 0.00000 0.00000
## Q121011.fctrYes 0.00000 0.00000
## Q121699.fctrYes 0.00000 0.00000
## Q121700.fctrNo 0.00000 0.00000
## Q121700.fctrYes 0.00000 0.00000
## Q122120.fctrNo 0.00000 0.00000
## Q122120.fctrYes 0.00000 0.00000
## Q122769.fctrNo 0.00000 0.00000
## Q122769.fctrYes 0.00000 0.00000
## Q122770.fctrNo 0.00000 0.00000
## Q122770.fctrYes 0.00000 0.00000
## Q122771.fctrPc 0.00000 0.00000
## Q122771.fctrPt 0.00000 0.00000
## Q123464.fctrNo 0.00000 0.00000
## Q123464.fctrYes 0.00000 0.00000
## Q123621.fctrNo 0.00000 0.00000
## Q123621.fctrYes 0.00000 0.00000
## Q124122.fctrNo 0.00000 0.00000
## Q124122.fctrYes 0.00000 0.00000
## Q124742.fctrNo 0.00000 0.00000
## Q124742.fctrYes 0.00000 0.00000
## Q96024.fctrNo 0.00000 0.00000
## Q96024.fctrYes 0.00000 0.00000
## Q98059.fctrOnly-child 0.00000 0.00000
## Q98059.fctrYes 0.00000 0.00000
## Q98078.fctrNo 0.00000 0.00000
## Q98078.fctrYes 0.00000 0.00000
## Q98197.fctrYes 0.00000 0.00000
## Q98578.fctrNo 0.00000 0.00000
## Q98578.fctrYes 0.00000 0.00000
## Q98869.fctrNo 0.00000 0.00000
## Q98869.fctrYes 0.00000 0.00000
## Q99480.fctrNo 0.00000 0.00000
## Q99480.fctrYes 0.00000 0.00000
## Q99581.fctrNo 0.00000 0.00000
## Q99581.fctrYes 0.00000 0.00000
## Q99716.fctrNo 0.00000 0.00000
## Q99716.fctrYes 0.00000 0.00000
## Q99982.fctrCheck! 0.00000 0.00000
## Q99982.fctrNope 0.00000 0.00000
## YOB.Age.fctr.L 0.00000 0.00000
## YOB.Age.fctr.Q 0.00000 0.00000
## YOB.Age.fctr.C 0.00000 0.00000
## YOB.Age.fctr^4 0.00000 0.00000
## YOB.Age.fctr^5 0.00000 0.00000
## YOB.Age.fctr^6 0.00000 0.00000
## YOB.Age.fctr^7 0.00000 0.00000
## YOB.Age.fctr^8 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr6 0.00000 0.00000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000 0.00000## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSltId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 2446 R 0.1738656
## 2 2749 R 0.1818018
## 3 4762 R 0.1926381
## 4 3895 R 0.1926381
## 5 5782 R 0.1967094
## 6 5800 R 0.1967094
## 7 1345 D 0.1776275
## 8 2882 D 0.1818018
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## 7 D FALSE
## 8 D FALSE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8261344 FALSE
## 2 0.8181982 FALSE
## 3 0.8073619 FALSE
## 4 0.8073619 FALSE
## 5 0.8032906 FALSE
## 6 0.8032906 FALSE
## 7 0.1776275 TRUE
## 8 0.1818018 TRUE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.3261344
## 2 FALSE -0.3181982
## 3 FALSE -0.3073619
## 4 FALSE -0.3073619
## 5 FALSE -0.3032906
## 6 FALSE -0.3032906
## 7 TRUE 0.0000000
## 8 TRUE 0.0000000
## .label
## 1 2446
## 2 2749
## 3 4762
## 4 3895
## 5 5782
## 6 5800
## 7 1345
## 8 2882
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1883 R 0.1620725
## 2 626 R 0.1620725
## 3 3212 R 0.1620725
## 4 597 R 0.1719800
## 5 2446 R 0.1738656
## 6 5377 R 0.1775328
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8379275 FALSE
## 2 0.8379275 FALSE
## 3 0.8379275 FALSE
## 4 0.8280200 FALSE
## 5 0.8261344 FALSE
## 6 0.8224672 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.3379275
## 2 FALSE -0.3379275
## 3 FALSE -0.3379275
## 4 FALSE -0.3280200
## 5 FALSE -0.3261344
## 6 FALSE -0.3224672
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 10 776 R 0.1834018
## 26 6045 R 0.1996198
## 28 3941 R 0.2001154
## 32 309 R 0.2085396
## 37 3640 R 0.2142266
## 41 445 R 0.2256551
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 10 D TRUE
## 26 D TRUE
## 28 D TRUE
## 32 D TRUE
## 37 D TRUE
## 41 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 10 0.8165982
## 26 0.8003802
## 28 0.7998846
## 32 0.7914604
## 37 0.7857734
## 41 0.7743449
## Party.fctr.All.X..rcv.glmnet.is.acc
## 10 FALSE
## 26 FALSE
## 28 FALSE
## 32 FALSE
## 37 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 10 FALSE
## 26 FALSE
## 28 FALSE
## 32 FALSE
## 37 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 10 -0.3165982
## 26 -0.3003802
## 28 -0.2998846
## 32 -0.2914604
## 37 -0.2857734
## 41 -0.2743449
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 36 4836 R 0.2110184
## 37 3640 R 0.2142266
## 38 2799 R 0.2212949
## 39 2252 R 0.2220632
## 40 6135 R 0.2229394
## 41 445 R 0.2256551
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 36 D TRUE
## 37 D TRUE
## 38 D TRUE
## 39 D TRUE
## 40 D TRUE
## 41 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 36 0.7889816
## 37 0.7857734
## 38 0.7787051
## 39 0.7779368
## 40 0.7770606
## 41 0.7743449
## Party.fctr.All.X..rcv.glmnet.is.acc
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 36 -0.2889816
## 37 -0.2857734
## 38 -0.2787051
## 39 -0.2779368
## 40 -0.2770606
## 41 -0.2743449
## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy PKy 3 8 2 0.01092896 0.01554404
## N N 9 40 10 0.05464481 0.04663212
## PKn PKn 9 41 10 0.05601093 0.04663212
## SKy SKy 9 37 10 0.05054645 0.04663212
## MKn MKn 23 100 26 0.13661202 0.11917098
## MKy MKy 47 182 55 0.24863388 0.24352332
## SKn SKn 93 324 110 0.44262295 0.48186528
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy 0.00896861 1.508677 0.1885847 8 1.224459
## N 0.04484305 11.989546 0.2997386 40 2.982814
## PKn 0.04484305 9.570359 0.2334234 41 2.931887
## SKy 0.04484305 10.006987 0.2704591 37 2.929034
## MKn 0.11659193 31.969380 0.3196938 100 7.454984
## MKy 0.24663677 54.608025 0.3000441 182 15.177415
## SKn 0.49327354 107.239135 0.3309850 324 29.553036
## err.abs.OOB.mean
## PKy 0.4081529
## N 0.3314237
## PKn 0.3257652
## SKy 0.3254483
## MKn 0.3241298
## MKy 0.3229237
## SKn 0.3177746
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 193.000000 732.000000 223.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 226.892109 1.942929
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 732.000000 62.253628 2.355618## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 335.504 NA NA## label step_major step_minor label_minor bgn end elapsed
## 6 fit.models 4 2 2 327.746 335.514 7.768
## 7 fit.models 4 3 3 335.514 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 fit.models 4 3 3 335.514 338.407
## 8 fit.data.training 5 0 0 338.408 NA
## elapsed
## 7 2.894
## 8 NAStep 5.0: fit data training
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Trn.All.X###glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## Fitting alpha = 0.55, lambda = 0.06 on full training set
## [1] "myfit_mdl: train complete: 2.699000 secs"
## alpha lambda
## 1 0.55 0.06
## Length Class Mode
## a0 85 -none- numeric
## beta 23290 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 274 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Q118232.fctrId Q120194.fctrStudy first
## -1.36615058 -0.08281527 -0.01418477
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^5 Q118232.fctrId
## -1.33952699 0.02124747 -0.11888072
## Q120194.fctrStudy first
## -0.04680377
## [1] "myfit_mdl: train diagnostics complete: 2.782000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.8021622 0
## 7 0.30 0 0.8021622 0
## 8 0.35 0 0.8021622 0
## 9 0.40 0 0.8021622 0
## 10 0.45 0 0.8021622 0
## 11 0.50 0 0.8021622 0
## 12 0.55 0 0.8021622 0
## 13 0.60 0 0.8021622 0
## 14 0.65 0 0.8021622 0
## 15 0.70 0 0.8021622 0
## 16 0.75 0 0.8021622 0
## 17 0.80 0 0.8021622 0
## 18 0.85 0 0.8021622 0
## 19 0.90 0 0.8021622 0
## 20 0.95 0 0.8021622 0
## 21 1.00 0 0.8021622 0
## Prediction
## Reference D R
## D 742 0
## R 183 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.021622e-01 0.000000e+00 7.750043e-01 8.273799e-01 8.021622e-01
## AccuracyPValue McnemarPValue
## 5.197662e-01 2.923411e-41
## [1] "myfit_mdl: predict complete: 7.398000 secs"
## id
## 1 Trn.All.X###glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.935 1.337
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.6097315
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8021622
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7750043 0.8273799 0
## [1] "myfit_mdl: exit: 7.424000 secs"
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Trn.All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.724000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0187 on full training set
## [1] "myfit_mdl: train complete: 76.594000 secs"## Warning in myfit_mdl(mdl_specs_lst = thsRslSpc, indepVar = mdlIndepVar, :
## model's bestTune found at an extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = thsRslSpc, indepVar = mdlIndepVar, :
## model's bestTune found at an extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 80 -none- numeric
## beta 21920 dgCMatrix S4
## df 80 -none- numeric
## dim 2 -none- numeric
## lambda 80 -none- numeric
## dev.ratio 80 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 274 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.2040238347 -0.2239819565
## Edn.fctr.C Edn.fctr^5
## 0.0427524941 0.2137548483
## Q100680.fctrNo Q100689.fctrNo
## 0.0912356066 0.0441719038
## Q102906.fctrYes Q106042.fctrNo
## 0.0126803079 0.0066756168
## Q110740.fctrMac Q112270.fctrNo
## -0.0914664274 -0.0230380809
## Q112478.fctrNo Q114152.fctrYes
## 0.1661697888 -0.1551429275
## Q114517.fctrYes Q115390.fctrYes
## -0.0813248108 -0.0090727963
## Q115611.fctrYes Q116197.fctrA.M.
## 0.0623701766 0.1283219414
## Q116953.fctrNo Q117186.fctrCool headed
## 0.0651753402 0.0660464850
## Q118232.fctrId Q119851.fctrNo
## -0.3578508375 0.0781881868
## Q120194.fctrStudy first Q120194.fctrTry first
## -0.2259730969 0.0135483308
## Q121699.fctrNo Q124122.fctrYes
## 0.1199838196 -0.0351489110
## Q124742.fctrNo Q98197.fctrNo
## -0.1117183147 -0.1859023685
## YOB.Age.fctr^7 Hhold.fctrSKn:.clusterid.fctr2
## 0.1469344388 0.1256689228
## YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.0002698872
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.206053754 -0.261776327
## Edn.fctr.C Edn.fctr^5
## 0.067730310 0.235727004
## Q100680.fctrNo Q100689.fctrNo
## 0.112664914 0.051483177
## Q102906.fctrYes Q106042.fctrNo
## 0.043874680 0.018788154
## Q110740.fctrMac Q112270.fctrNo
## -0.110908901 -0.041108937
## Q112478.fctrNo Q114152.fctrYes
## 0.188708252 -0.184675605
## Q114517.fctrYes Q115390.fctrYes
## -0.112738388 -0.037194765
## Q115611.fctrYes Q116197.fctrA.M.
## 0.092379930 0.153940620
## Q116197.fctrP.M. Q116953.fctrNo
## -0.004250445 0.083540436
## Q117186.fctrCool headed Q118232.fctrId
## 0.100781243 -0.388895995
## Q119851.fctrNo Q120194.fctrStudy first
## 0.104518652 -0.238219108
## Q120194.fctrTry first Q121699.fctrNo
## 0.035677091 0.132213914
## Q124122.fctrYes Q124742.fctrNo
## -0.048665076 -0.136500248
## Q98197.fctrNo YOB.Age.fctr^7
## -0.216622520 0.185811944
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr5
## 0.146835838 0.035345298
## Hhold.fctrSKy:.clusterid.fctr5
## 0.003609091
## [1] "myfit_mdl: train diagnostics complete: 77.264000 secs"
## Prediction
## Reference D R
## D 731 11
## R 171 12
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.032432e-01 7.567039e-02 7.761347e-01 8.284047e-01 8.021622e-01
## AccuracyPValue McnemarPValue
## 4.868548e-01 4.616608e-32
## [1] "myfit_mdl: predict complete: 82.610000 secs"
## id
## 1 Trn.All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 75.789 1.445
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.722696
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.1165049 0.8014414
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7761347 0.8284047 0.0007281275
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.001404815 0.005063715
## [1] "myfit_mdl: exit: 82.633000 secs"## label step_major step_minor label_minor bgn end
## 8 fit.data.training 5 0 0 338.408 428.949
## 9 fit.data.training 5 1 1 428.950 NA
## elapsed
## 8 90.541
## 9 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSltId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFnlNslId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFnlNslId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glbMdlFnlNsl uses the same coefficients as glbMdlSlt,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Trn.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Trn.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFnlNslId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFnlNslId, :
## Using default probability threshold: 0.5glb_featsimp_df <- myget_feats_importance(mdl=glbMdlFnlNsl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFnlNslId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp
## Q118232.fctrId 53.71026
## Q120194.fctrStudy first 64.68834
## Edn.fctr^5 100.00000
## .rnorm 0.00000
## Edn.fctr.C 0.00000
## Edn.fctr.L 0.00000
## Edn.fctr.Q 52.48374
## Edn.fctr^4 0.00000
## Edn.fctr^6 0.00000
## Edn.fctr^7 0.00000
## Gender.fctrF 0.00000
## Gender.fctrM 0.00000
## Hhold.fctrMKn 0.00000
## Hhold.fctrMKn:.clusterid.fctr2 0.00000
## Hhold.fctrMKn:.clusterid.fctr3 0.00000
## Hhold.fctrMKn:.clusterid.fctr4 0.00000
## Hhold.fctrMKn:.clusterid.fctr5 0.00000
## Hhold.fctrMKn:.clusterid.fctr6 0.00000
## Hhold.fctrMKy 0.00000
## Hhold.fctrMKy:.clusterid.fctr2 0.00000
## Hhold.fctrMKy:.clusterid.fctr3 0.00000
## Hhold.fctrMKy:.clusterid.fctr4 0.00000
## Hhold.fctrMKy:.clusterid.fctr5 0.00000
## Hhold.fctrMKy:.clusterid.fctr6 0.00000
## Hhold.fctrN:.clusterid.fctr2 0.00000
## Hhold.fctrN:.clusterid.fctr3 0.00000
## Hhold.fctrN:.clusterid.fctr4 0.00000
## Hhold.fctrN:.clusterid.fctr5 0.00000
## Hhold.fctrN:.clusterid.fctr6 0.00000
## Hhold.fctrPKn 0.00000
## Hhold.fctrPKn:.clusterid.fctr2 0.00000
## Hhold.fctrPKn:.clusterid.fctr3 0.00000
## Hhold.fctrPKn:.clusterid.fctr4 0.00000
## Hhold.fctrPKn:.clusterid.fctr5 0.00000
## Hhold.fctrPKn:.clusterid.fctr6 0.00000
## Hhold.fctrPKy 0.00000
## Hhold.fctrPKy:.clusterid.fctr2 0.00000
## Hhold.fctrPKy:.clusterid.fctr3 0.00000
## Hhold.fctrPKy:.clusterid.fctr4 0.00000
## Hhold.fctrPKy:.clusterid.fctr5 0.00000
## Hhold.fctrPKy:.clusterid.fctr6 0.00000
## Hhold.fctrSKn 0.00000
## Hhold.fctrSKn:.clusterid.fctr2 23.05489
## Hhold.fctrSKn:.clusterid.fctr3 0.00000
## Hhold.fctrSKn:.clusterid.fctr4 0.00000
## Hhold.fctrSKn:.clusterid.fctr5 0.00000
## Hhold.fctrSKn:.clusterid.fctr6 0.00000
## Hhold.fctrSKy 0.00000
## Hhold.fctrSKy:.clusterid.fctr2 0.00000
## Hhold.fctrSKy:.clusterid.fctr3 0.00000
## Hhold.fctrSKy:.clusterid.fctr4 0.00000
## Hhold.fctrSKy:.clusterid.fctr5 0.00000
## Hhold.fctrSKy:.clusterid.fctr6 0.00000
## Income.fctr.C 0.00000
## Income.fctr.L 0.00000
## Income.fctr.Q 0.00000
## Income.fctr^4 0.00000
## Income.fctr^5 0.00000
## Income.fctr^6 0.00000
## Q100010.fctrNo 0.00000
## Q100010.fctrYes 0.00000
## Q100562.fctrNo 0.00000
## Q100562.fctrYes 0.00000
## Q100680.fctrNo 0.00000
## Q100680.fctrYes 0.00000
## Q100689.fctrNo 0.00000
## Q100689.fctrYes 0.00000
## Q101162.fctrOptimist 0.00000
## Q101162.fctrPessimist 0.00000
## Q101163.fctrDad 0.00000
## Q101163.fctrMom 0.00000
## Q101596.fctrNo 0.00000
## Q101596.fctrYes 0.00000
## Q102089.fctrOwn 0.00000
## Q102089.fctrRent 0.00000
## Q102289.fctrNo 0.00000
## Q102289.fctrYes 0.00000
## Q102674.fctrNo 0.00000
## Q102674.fctrYes 0.00000
## Q102687.fctrNo 0.00000
## Q102687.fctrYes 0.00000
## Q102906.fctrNo 0.00000
## Q102906.fctrYes 0.00000
## Q103293.fctrNo 0.00000
## Q103293.fctrYes 0.00000
## Q104996.fctrNo 0.00000
## Q104996.fctrYes 0.00000
## Q105655.fctrNo 0.00000
## Q105655.fctrYes 0.00000
## Q105840.fctrNo 0.00000
## Q105840.fctrYes 0.00000
## Q106042.fctrNo 0.00000
## Q106042.fctrYes 0.00000
## Q106272.fctrNo 0.00000
## Q106272.fctrYes 0.00000
## Q106388.fctrNo 0.00000
## Q106388.fctrYes 0.00000
## Q106389.fctrNo 0.00000
## Q106389.fctrYes 0.00000
## Q106993.fctrNo 0.00000
## Q106993.fctrYes 0.00000
## Q106997.fctrGr 0.00000
## Q106997.fctrYy 0.00000
## Q107491.fctrNo 0.00000
## Q107491.fctrYes 0.00000
## Q107869.fctrNo 0.00000
## Q107869.fctrYes 0.00000
## Q108342.fctrIn-person 0.00000
## Q108342.fctrOnline 0.00000
## Q108343.fctrNo 0.00000
## Q108343.fctrYes 0.00000
## Q108617.fctrNo 0.00000
## Q108617.fctrYes 0.00000
## Q108754.fctrNo 0.00000
## Q108754.fctrYes 0.00000
## Q108855.fctrUmm... 0.00000
## Q108855.fctrYes! 0.00000
## Q108856.fctrSocialize 0.00000
## Q108856.fctrSpace 0.00000
## Q108950.fctrCautious 0.00000
## Q108950.fctrRisk-friendly 0.00000
## Q109367.fctrNo 0.00000
## Q109367.fctrYes 0.00000
## Q110740.fctrMac 0.00000
## Q110740.fctrPC 0.00000
## Q111220.fctrNo 0.00000
## Q111220.fctrYes 0.00000
## Q111580.fctrDemanding 0.00000
## Q111580.fctrSupportive 0.00000
## Q111848.fctrNo 0.00000
## Q111848.fctrYes 0.00000
## Q112270.fctrNo 0.00000
## Q112270.fctrYes 0.00000
## Q112478.fctrNo 0.00000
## Q112478.fctrYes 0.00000
## Q112512.fctrNo 0.00000
## Q112512.fctrYes 0.00000
## Q113181.fctrNo 0.00000
## Q113181.fctrYes 0.00000
## Q113583.fctrTalk 0.00000
## Q113583.fctrTunes 0.00000
## Q113584.fctrPeople 0.00000
## Q113584.fctrTechnology 0.00000
## Q113992.fctrNo 0.00000
## Q113992.fctrYes 0.00000
## Q114152.fctrNo 0.00000
## Q114152.fctrYes 0.00000
## Q114386.fctrMysterious 0.00000
## Q114386.fctrTMI 0.00000
## Q114517.fctrNo 0.00000
## Q114517.fctrYes 0.00000
## Q114748.fctrNo 0.00000
## Q114748.fctrYes 0.00000
## Q114961.fctrNo 0.00000
## Q114961.fctrYes 0.00000
## Q115195.fctrNo 0.00000
## Q115195.fctrYes 0.00000
## Q115390.fctrNo 0.00000
## Q115390.fctrYes 0.00000
## Q115602.fctrNo 0.00000
## Q115602.fctrYes 0.00000
## Q115610.fctrNo 0.00000
## Q115610.fctrYes 0.00000
## Q115611.fctrNo 0.00000
## Q115611.fctrYes 0.00000
## Q115777.fctrEnd 0.00000
## Q115777.fctrStart 0.00000
## Q115899.fctrCs 0.00000
## Q115899.fctrMe 0.00000
## Q116197.fctrA.M. 0.00000
## Q116197.fctrP.M. 0.00000
## Q116441.fctrNo 0.00000
## Q116441.fctrYes 0.00000
## Q116448.fctrNo 0.00000
## Q116448.fctrYes 0.00000
## Q116601.fctrNo 0.00000
## Q116601.fctrYes 0.00000
## Q116797.fctrNo 0.00000
## Q116797.fctrYes 0.00000
## Q116881.fctrHappy 0.00000
## Q116881.fctrRight 0.00000
## Q116953.fctrNo 0.00000
## Q116953.fctrYes 0.00000
## Q117186.fctrCool headed 0.00000
## Q117186.fctrHot headed 0.00000
## Q117193.fctrOdd hours 0.00000
## Q117193.fctrStandard hours 0.00000
## Q118117.fctrNo 0.00000
## Q118117.fctrYes 0.00000
## Q118232.fctrPr 0.00000
## Q118233.fctrNo 0.00000
## Q118233.fctrYes 0.00000
## Q118237.fctrNo 0.00000
## Q118237.fctrYes 0.00000
## Q118892.fctrNo 0.00000
## Q118892.fctrYes 0.00000
## Q119334.fctrNo 0.00000
## Q119334.fctrYes 0.00000
## Q119650.fctrGiving 0.00000
## Q119650.fctrReceiving 0.00000
## Q119851.fctrNo 0.00000
## Q119851.fctrYes 0.00000
## Q120012.fctrNo 0.00000
## Q120012.fctrYes 0.00000
## Q120014.fctrNo 0.00000
## Q120014.fctrYes 0.00000
## Q120194.fctrTry first 0.00000
## Q120379.fctrNo 0.00000
## Q120379.fctrYes 0.00000
## Q120472.fctrArt 0.00000
## Q120472.fctrScience 0.00000
## Q120650.fctrNo 0.00000
## Q120650.fctrYes 0.00000
## Q120978.fctrNo 0.00000
## Q120978.fctrYes 0.00000
## Q121011.fctrNo 0.00000
## Q121011.fctrYes 0.00000
## Q121699.fctrNo 18.94674
## Q121699.fctrYes 0.00000
## Q121700.fctrNo 0.00000
## Q121700.fctrYes 0.00000
## Q122120.fctrNo 0.00000
## Q122120.fctrYes 0.00000
## Q122769.fctrNo 0.00000
## Q122769.fctrYes 0.00000
## Q122770.fctrNo 0.00000
## Q122770.fctrYes 0.00000
## Q122771.fctrPc 0.00000
## Q122771.fctrPt 0.00000
## Q123464.fctrNo 0.00000
## Q123464.fctrYes 0.00000
## Q123621.fctrNo 0.00000
## Q123621.fctrYes 0.00000
## Q124122.fctrNo 0.00000
## Q124122.fctrYes 0.00000
## Q124742.fctrNo 0.00000
## Q124742.fctrYes 0.00000
## Q96024.fctrNo 0.00000
## Q96024.fctrYes 0.00000
## Q98059.fctrOnly-child 0.00000
## Q98059.fctrYes 0.00000
## Q98078.fctrNo 0.00000
## Q98078.fctrYes 0.00000
## Q98197.fctrNo 73.29298
## Q98197.fctrYes 0.00000
## Q98578.fctrNo 0.00000
## Q98578.fctrYes 0.00000
## Q98869.fctrNo 0.00000
## Q98869.fctrYes 0.00000
## Q99480.fctrNo 0.00000
## Q99480.fctrYes 0.00000
## Q99581.fctrNo 0.00000
## Q99581.fctrYes 0.00000
## Q99716.fctrNo 0.00000
## Q99716.fctrYes 0.00000
## Q99982.fctrCheck! 0.00000
## Q99982.fctrNope 0.00000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.00000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000
## YOB.Age.fctr.C 0.00000
## YOB.Age.fctr.L 0.00000
## YOB.Age.fctr.Q 0.00000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000
## YOB.Age.fctr^4 0.00000
## YOB.Age.fctr^5 0.00000
## YOB.Age.fctr^6 0.00000
## YOB.Age.fctr^7 0.00000
## YOB.Age.fctr^8 0.00000
## Trn.All.X...glmnet.imp imp
## Q118232.fctrId 100.00000 100.00000
## Q120194.fctrStudy first 38.66380 38.66380
## Edn.fctr^5 17.30517 17.30517
## .rnorm 0.00000 0.00000
## Edn.fctr.C 0.00000 0.00000
## Edn.fctr.L 0.00000 0.00000
## Edn.fctr.Q 0.00000 0.00000
## Edn.fctr^4 0.00000 0.00000
## Edn.fctr^6 0.00000 0.00000
## Edn.fctr^7 0.00000 0.00000
## Gender.fctrF 0.00000 0.00000
## Gender.fctrM 0.00000 0.00000
## Hhold.fctrMKn 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrMKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrMKy 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrMKy:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrN:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrPKn 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrPKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrPKy 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrPKy:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrSKn 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrSKn:.clusterid.fctr6 0.00000 0.00000
## Hhold.fctrSKy 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr2 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr3 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr4 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr5 0.00000 0.00000
## Hhold.fctrSKy:.clusterid.fctr6 0.00000 0.00000
## Income.fctr.C 0.00000 0.00000
## Income.fctr.L 0.00000 0.00000
## Income.fctr.Q 0.00000 0.00000
## Income.fctr^4 0.00000 0.00000
## Income.fctr^5 0.00000 0.00000
## Income.fctr^6 0.00000 0.00000
## Q100010.fctrNo 0.00000 0.00000
## Q100010.fctrYes 0.00000 0.00000
## Q100562.fctrNo 0.00000 0.00000
## Q100562.fctrYes 0.00000 0.00000
## Q100680.fctrNo 0.00000 0.00000
## Q100680.fctrYes 0.00000 0.00000
## Q100689.fctrNo 0.00000 0.00000
## Q100689.fctrYes 0.00000 0.00000
## Q101162.fctrOptimist 0.00000 0.00000
## Q101162.fctrPessimist 0.00000 0.00000
## Q101163.fctrDad 0.00000 0.00000
## Q101163.fctrMom 0.00000 0.00000
## Q101596.fctrNo 0.00000 0.00000
## Q101596.fctrYes 0.00000 0.00000
## Q102089.fctrOwn 0.00000 0.00000
## Q102089.fctrRent 0.00000 0.00000
## Q102289.fctrNo 0.00000 0.00000
## Q102289.fctrYes 0.00000 0.00000
## Q102674.fctrNo 0.00000 0.00000
## Q102674.fctrYes 0.00000 0.00000
## Q102687.fctrNo 0.00000 0.00000
## Q102687.fctrYes 0.00000 0.00000
## Q102906.fctrNo 0.00000 0.00000
## Q102906.fctrYes 0.00000 0.00000
## Q103293.fctrNo 0.00000 0.00000
## Q103293.fctrYes 0.00000 0.00000
## Q104996.fctrNo 0.00000 0.00000
## Q104996.fctrYes 0.00000 0.00000
## Q105655.fctrNo 0.00000 0.00000
## Q105655.fctrYes 0.00000 0.00000
## Q105840.fctrNo 0.00000 0.00000
## Q105840.fctrYes 0.00000 0.00000
## Q106042.fctrNo 0.00000 0.00000
## Q106042.fctrYes 0.00000 0.00000
## Q106272.fctrNo 0.00000 0.00000
## Q106272.fctrYes 0.00000 0.00000
## Q106388.fctrNo 0.00000 0.00000
## Q106388.fctrYes 0.00000 0.00000
## Q106389.fctrNo 0.00000 0.00000
## Q106389.fctrYes 0.00000 0.00000
## Q106993.fctrNo 0.00000 0.00000
## Q106993.fctrYes 0.00000 0.00000
## Q106997.fctrGr 0.00000 0.00000
## Q106997.fctrYy 0.00000 0.00000
## Q107491.fctrNo 0.00000 0.00000
## Q107491.fctrYes 0.00000 0.00000
## Q107869.fctrNo 0.00000 0.00000
## Q107869.fctrYes 0.00000 0.00000
## Q108342.fctrIn-person 0.00000 0.00000
## Q108342.fctrOnline 0.00000 0.00000
## Q108343.fctrNo 0.00000 0.00000
## Q108343.fctrYes 0.00000 0.00000
## Q108617.fctrNo 0.00000 0.00000
## Q108617.fctrYes 0.00000 0.00000
## Q108754.fctrNo 0.00000 0.00000
## Q108754.fctrYes 0.00000 0.00000
## Q108855.fctrUmm... 0.00000 0.00000
## Q108855.fctrYes! 0.00000 0.00000
## Q108856.fctrSocialize 0.00000 0.00000
## Q108856.fctrSpace 0.00000 0.00000
## Q108950.fctrCautious 0.00000 0.00000
## Q108950.fctrRisk-friendly 0.00000 0.00000
## Q109367.fctrNo 0.00000 0.00000
## Q109367.fctrYes 0.00000 0.00000
## Q110740.fctrMac 0.00000 0.00000
## Q110740.fctrPC 0.00000 0.00000
## Q111220.fctrNo 0.00000 0.00000
## Q111220.fctrYes 0.00000 0.00000
## Q111580.fctrDemanding 0.00000 0.00000
## Q111580.fctrSupportive 0.00000 0.00000
## Q111848.fctrNo 0.00000 0.00000
## Q111848.fctrYes 0.00000 0.00000
## Q112270.fctrNo 0.00000 0.00000
## Q112270.fctrYes 0.00000 0.00000
## Q112478.fctrNo 0.00000 0.00000
## Q112478.fctrYes 0.00000 0.00000
## Q112512.fctrNo 0.00000 0.00000
## Q112512.fctrYes 0.00000 0.00000
## Q113181.fctrNo 0.00000 0.00000
## Q113181.fctrYes 0.00000 0.00000
## Q113583.fctrTalk 0.00000 0.00000
## Q113583.fctrTunes 0.00000 0.00000
## Q113584.fctrPeople 0.00000 0.00000
## Q113584.fctrTechnology 0.00000 0.00000
## Q113992.fctrNo 0.00000 0.00000
## Q113992.fctrYes 0.00000 0.00000
## Q114152.fctrNo 0.00000 0.00000
## Q114152.fctrYes 0.00000 0.00000
## Q114386.fctrMysterious 0.00000 0.00000
## Q114386.fctrTMI 0.00000 0.00000
## Q114517.fctrNo 0.00000 0.00000
## Q114517.fctrYes 0.00000 0.00000
## Q114748.fctrNo 0.00000 0.00000
## Q114748.fctrYes 0.00000 0.00000
## Q114961.fctrNo 0.00000 0.00000
## Q114961.fctrYes 0.00000 0.00000
## Q115195.fctrNo 0.00000 0.00000
## Q115195.fctrYes 0.00000 0.00000
## Q115390.fctrNo 0.00000 0.00000
## Q115390.fctrYes 0.00000 0.00000
## Q115602.fctrNo 0.00000 0.00000
## Q115602.fctrYes 0.00000 0.00000
## Q115610.fctrNo 0.00000 0.00000
## Q115610.fctrYes 0.00000 0.00000
## Q115611.fctrNo 0.00000 0.00000
## Q115611.fctrYes 0.00000 0.00000
## Q115777.fctrEnd 0.00000 0.00000
## Q115777.fctrStart 0.00000 0.00000
## Q115899.fctrCs 0.00000 0.00000
## Q115899.fctrMe 0.00000 0.00000
## Q116197.fctrA.M. 0.00000 0.00000
## Q116197.fctrP.M. 0.00000 0.00000
## Q116441.fctrNo 0.00000 0.00000
## Q116441.fctrYes 0.00000 0.00000
## Q116448.fctrNo 0.00000 0.00000
## Q116448.fctrYes 0.00000 0.00000
## Q116601.fctrNo 0.00000 0.00000
## Q116601.fctrYes 0.00000 0.00000
## Q116797.fctrNo 0.00000 0.00000
## Q116797.fctrYes 0.00000 0.00000
## Q116881.fctrHappy 0.00000 0.00000
## Q116881.fctrRight 0.00000 0.00000
## Q116953.fctrNo 0.00000 0.00000
## Q116953.fctrYes 0.00000 0.00000
## Q117186.fctrCool headed 0.00000 0.00000
## Q117186.fctrHot headed 0.00000 0.00000
## Q117193.fctrOdd hours 0.00000 0.00000
## Q117193.fctrStandard hours 0.00000 0.00000
## Q118117.fctrNo 0.00000 0.00000
## Q118117.fctrYes 0.00000 0.00000
## Q118232.fctrPr 0.00000 0.00000
## Q118233.fctrNo 0.00000 0.00000
## Q118233.fctrYes 0.00000 0.00000
## Q118237.fctrNo 0.00000 0.00000
## Q118237.fctrYes 0.00000 0.00000
## Q118892.fctrNo 0.00000 0.00000
## Q118892.fctrYes 0.00000 0.00000
## Q119334.fctrNo 0.00000 0.00000
## Q119334.fctrYes 0.00000 0.00000
## Q119650.fctrGiving 0.00000 0.00000
## Q119650.fctrReceiving 0.00000 0.00000
## Q119851.fctrNo 0.00000 0.00000
## Q119851.fctrYes 0.00000 0.00000
## Q120012.fctrNo 0.00000 0.00000
## Q120012.fctrYes 0.00000 0.00000
## Q120014.fctrNo 0.00000 0.00000
## Q120014.fctrYes 0.00000 0.00000
## Q120194.fctrTry first 0.00000 0.00000
## Q120379.fctrNo 0.00000 0.00000
## Q120379.fctrYes 0.00000 0.00000
## Q120472.fctrArt 0.00000 0.00000
## Q120472.fctrScience 0.00000 0.00000
## Q120650.fctrNo 0.00000 0.00000
## Q120650.fctrYes 0.00000 0.00000
## Q120978.fctrNo 0.00000 0.00000
## Q120978.fctrYes 0.00000 0.00000
## Q121011.fctrNo 0.00000 0.00000
## Q121011.fctrYes 0.00000 0.00000
## Q121699.fctrNo 0.00000 0.00000
## Q121699.fctrYes 0.00000 0.00000
## Q121700.fctrNo 0.00000 0.00000
## Q121700.fctrYes 0.00000 0.00000
## Q122120.fctrNo 0.00000 0.00000
## Q122120.fctrYes 0.00000 0.00000
## Q122769.fctrNo 0.00000 0.00000
## Q122769.fctrYes 0.00000 0.00000
## Q122770.fctrNo 0.00000 0.00000
## Q122770.fctrYes 0.00000 0.00000
## Q122771.fctrPc 0.00000 0.00000
## Q122771.fctrPt 0.00000 0.00000
## Q123464.fctrNo 0.00000 0.00000
## Q123464.fctrYes 0.00000 0.00000
## Q123621.fctrNo 0.00000 0.00000
## Q123621.fctrYes 0.00000 0.00000
## Q124122.fctrNo 0.00000 0.00000
## Q124122.fctrYes 0.00000 0.00000
## Q124742.fctrNo 0.00000 0.00000
## Q124742.fctrYes 0.00000 0.00000
## Q96024.fctrNo 0.00000 0.00000
## Q96024.fctrYes 0.00000 0.00000
## Q98059.fctrOnly-child 0.00000 0.00000
## Q98059.fctrYes 0.00000 0.00000
## Q98078.fctrNo 0.00000 0.00000
## Q98078.fctrYes 0.00000 0.00000
## Q98197.fctrNo 0.00000 0.00000
## Q98197.fctrYes 0.00000 0.00000
## Q98578.fctrNo 0.00000 0.00000
## Q98578.fctrYes 0.00000 0.00000
## Q98869.fctrNo 0.00000 0.00000
## Q98869.fctrYes 0.00000 0.00000
## Q99480.fctrNo 0.00000 0.00000
## Q99480.fctrYes 0.00000 0.00000
## Q99581.fctrNo 0.00000 0.00000
## Q99581.fctrYes 0.00000 0.00000
## Q99716.fctrNo 0.00000 0.00000
## Q99716.fctrYes 0.00000 0.00000
## Q99982.fctrCheck! 0.00000 0.00000
## Q99982.fctrNope 0.00000 0.00000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr.C 0.00000 0.00000
## YOB.Age.fctr.L 0.00000 0.00000
## YOB.Age.fctr.Q 0.00000 0.00000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000 0.00000
## YOB.Age.fctr^4 0.00000 0.00000
## YOB.Age.fctr^5 0.00000 0.00000
## YOB.Age.fctr^6 0.00000 0.00000
## YOB.Age.fctr^7 0.00000 0.00000
## YOB.Age.fctr^8 0.00000 0.00000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFnlNslId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSltId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFnlNslId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFnlNslId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 626 R NA
## 2 1883 R NA
## 3 3212 R NA
## 4 3473 R 0.1620725
## 5 6114 R 0.1923105
## 6 5638 R 0.1638709
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 <NA> NA
## 2 <NA> NA
## 3 <NA> NA
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 0.8379275 FALSE
## 5 0.8076895 FALSE
## 6 0.8361291 FALSE
## Party.fctr.Trn.All.X...glmnet.prob Party.fctr.Trn.All.X...glmnet
## 1 0.1804493 D
## 2 0.1804493 D
## 3 0.1804493 D
## 4 0.1804493 D
## 5 0.1809636 D
## 6 0.1814790 D
## Party.fctr.Trn.All.X...glmnet.err Party.fctr.Trn.All.X...glmnet.err.abs
## 1 TRUE 0.8195507
## 2 TRUE 0.8195507
## 3 TRUE 0.8195507
## 4 TRUE 0.8195507
## 5 TRUE 0.8190364
## 6 TRUE 0.8185210
## Party.fctr.Trn.All.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Trn.All.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Trn.All.X...glmnet.error
## 1 -0.3195507
## 2 -0.3195507
## 3 -0.3195507
## 4 -0.3195507
## 5 -0.3190364
## 6 -0.3185210
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 22 4600 R 0.1796686
## 30 5782 R NA
## 60 5230 R 0.1844984
## 99 3157 R NA
## 134 3895 R NA
## 164 4414 R 0.2057262
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 22 D TRUE
## 30 <NA> NA
## 60 D TRUE
## 99 <NA> NA
## 134 <NA> NA
## 164 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 22 0.8203314
## 30 NA
## 60 0.8155016
## 99 NA
## 134 NA
## 164 0.7942738
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Trn.All.X...glmnet.prob
## 22 FALSE 0.1876482
## 30 NA 0.1892419
## 60 FALSE 0.1989971
## 99 NA 0.2057541
## 134 NA 0.2078910
## 164 FALSE 0.2086069
## Party.fctr.Trn.All.X...glmnet Party.fctr.Trn.All.X...glmnet.err
## 22 D TRUE
## 30 D TRUE
## 60 D TRUE
## 99 D TRUE
## 134 D TRUE
## 164 D TRUE
## Party.fctr.Trn.All.X...glmnet.err.abs
## 22 0.8123518
## 30 0.8107581
## 60 0.8010029
## 99 0.7942459
## 134 0.7921090
## 164 0.7913931
## Party.fctr.Trn.All.X...glmnet.is.acc
## 22 FALSE
## 30 FALSE
## 60 FALSE
## 99 FALSE
## 134 FALSE
## 164 FALSE
## Party.fctr.Trn.All.X...glmnet.accurate
## 22 FALSE
## 30 FALSE
## 60 FALSE
## 99 FALSE
## 134 FALSE
## 164 FALSE
## Party.fctr.Trn.All.X...glmnet.error
## 22 -0.3123518
## 30 -0.3107581
## 60 -0.3010029
## 99 -0.2942459
## 134 -0.2921090
## 164 -0.2913931
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 178 5144 R 0.2256551
## 179 5420 R NA
## 180 5466 R NA
## 181 5484 R 0.2256551
## 182 5856 R 0.2212949
## 183 6099 R 0.2212949
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 178 D TRUE
## 179 <NA> NA
## 180 <NA> NA
## 181 D TRUE
## 182 D TRUE
## 183 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 178 0.7743449
## 179 NA
## 180 NA
## 181 0.7743449
## 182 0.7787051
## 183 0.7787051
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Trn.All.X...glmnet.prob
## 178 FALSE 0.2090374
## 179 NA 0.2090374
## 180 NA 0.2090374
## 181 FALSE 0.2090374
## 182 FALSE 0.2090374
## 183 FALSE 0.2090374
## Party.fctr.Trn.All.X...glmnet Party.fctr.Trn.All.X...glmnet.err
## 178 D TRUE
## 179 D TRUE
## 180 D TRUE
## 181 D TRUE
## 182 D TRUE
## 183 D TRUE
## Party.fctr.Trn.All.X...glmnet.err.abs
## 178 0.7909626
## 179 0.7909626
## 180 0.7909626
## 181 0.7909626
## 182 0.7909626
## 183 0.7909626
## Party.fctr.Trn.All.X...glmnet.is.acc
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## Party.fctr.Trn.All.X...glmnet.accurate
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## Party.fctr.Trn.All.X...glmnet.error
## 178 -0.2909626
## 179 -0.2909626
## 180 -0.2909626
## 181 -0.2909626
## 182 -0.2909626
## 183 -0.2909626
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Trn.All.X...glmnet.prob"
## [2] "Party.fctr.Trn.All.X...glmnet"
## [3] "Party.fctr.Trn.All.X...glmnet.err"
## [4] "Party.fctr.Trn.All.X...glmnet.err.abs"
## [5] "Party.fctr.Trn.All.X...glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 9 fit.data.training 5 1 1 428.950 441.666
## 10 predict.data.new 6 0 0 441.666 NA
## elapsed
## 9 12.716
## 10 NAStep 6.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlNslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlRslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlNslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlRslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFnlNslId, : Limiting important feature scatter plots to 5 out of 108## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## NULL## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] "Stacking file Q109244NA_Ensemble_cnk03_rest_out_fin.csv to prediction outputs..."## [1] 0.5## [1] "glbMdlSltId: All.X##rcv#glmnet"## [1] "glbMdlFnlNslId: Trn.All.X###glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y##rcv#rpart
## 0 1
## Trn.All.X###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.7875648 0.5698813
## Low.cor.X##rcv#glmnet 0.7875648 0.5547176
## Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## Random###myrandom_classfr 0.7875648 0.5012035
## MFO###myMFO_classfr 0.7875648 0.5000000
## Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## Trn.All.X###glmnet NA NA
## Trn.All.X##rcv#glmnet NA NA
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.5000000 18.247
## Low.cor.X##rcv#glmnet 0.5000000 19.028
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.792
## Random###myrandom_classfr 0.4922978 0.268
## MFO###myMFO_classfr 0.5000000 0.499
## Max.cor.Y##rcv#rpart 0.5000000 1.382
## Interact.High.cor.Y##rcv#glmnet 0.5000000 1.800
## Trn.All.X###glmnet NA 1.935
## Trn.All.X##rcv#glmnet NA 75.789
## max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet 0.8060121 0.50
## Low.cor.X##rcv#glmnet 0.8032761 0.30
## Max.cor.Y.rcv.1X1###glmnet 0.8060109 0.50
## Random###myrandom_classfr 0.8060109 0.85
## MFO###myMFO_classfr 0.8060109 0.50
## Max.cor.Y##rcv#rpart 0.8060121 0.50
## Interact.High.cor.Y##rcv#glmnet 0.8060121 0.50
## Trn.All.X###glmnet 0.8021622 0.50
## Trn.All.X##rcv#glmnet 0.8014414 0.30
## opt.prob.threshold.OOB
## All.X##rcv#glmnet 0.50
## Low.cor.X##rcv#glmnet 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.50
## Random###myrandom_classfr 0.85
## MFO###myMFO_classfr 0.50
## Max.cor.Y##rcv#rpart 0.50
## Interact.High.cor.Y##rcv#glmnet 0.50
## Trn.All.X###glmnet NA
## Trn.All.X##rcv#glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 152 0
## R 41 0## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy 1.508677 1.224459 2.756856 NA
## N 11.989546 2.982814 15.086937 NA
## PKn 9.570359 2.931887 12.796200 NA
## SKy 10.006987 2.929034 13.128640 NA
## MKn 31.969380 7.454984 40.095890 NA
## MKy 54.608025 15.177415 70.959270 NA
## SKn 107.239135 29.553036 137.745873 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.OOB
## PKy 0.01092896 0.01554404 0.00896861 8 2 3
## N 0.05464481 0.04663212 0.04484305 40 10 9
## PKn 0.05601093 0.04663212 0.04484305 41 10 9
## SKy 0.05054645 0.04663212 0.04484305 37 10 9
## MKn 0.13661202 0.11917098 0.11659193 100 26 23
## MKy 0.24863388 0.24352332 0.24663677 182 55 47
## SKn 0.44262295 0.48186528 0.49327354 324 110 93
## .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy 10 1 2 8 2 11 0.4081529
## N 40 9 10 40 10 49 0.3314237
## PKn 45 5 10 41 10 50 0.3257652
## SKy 39 7 10 37 10 46 0.3254483
## MKn 97 26 26 100 26 123 0.3241298
## MKy 186 43 55 182 55 229 0.3229237
## SKn 325 92 110 324 110 417 0.3177746
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy 0.1885847 NA 0.2506232
## N 0.2997386 NA 0.3078967
## PKn 0.2334234 NA 0.2559240
## SKy 0.2704591 NA 0.2854052
## MKn 0.3196938 NA 0.3259828
## MKy 0.3000441 NA 0.3098658
## SKn 0.3309850 NA 0.3303258
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 226.892109 62.253628 292.569664 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 732.000000
## .n.New.D .n.OOB .n.Trn.D .n.Trn.R
## 223.000000 193.000000 742.000000 183.000000
## .n.Tst .n.fit .n.new .n.trn
## 223.000000 732.000000 223.000000 925.000000
## err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## 2.355618 1.942929 NA 2.066024## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Edn.fctr^5 100.00000
## Q98197.fctrNo 73.29298
## Q120194.fctrStudy first 64.68834
## Q118232.fctrId 53.71026
## Edn.fctr.Q 52.48374
## Hhold.fctrSKn:.clusterid.fctr2 23.05489
## Q121699.fctrNo 18.94674
## Trn.All.X...glmnet.imp
## Edn.fctr^5 17.30517
## Q98197.fctrNo 0.00000
## Q120194.fctrStudy first 38.66380
## Q118232.fctrId 100.00000
## Edn.fctr.Q 0.00000
## Hhold.fctrSKn:.clusterid.fctr2 0.00000
## Q121699.fctrNo 0.00000## [1] "glbObsNew prediction stats:"##
## D R
## 223 0## label step_major step_minor label_minor bgn end
## 10 predict.data.new 6 0 0 441.666 453.513
## 11 display.session.info 7 0 0 453.514 NA
## elapsed
## 10 11.847
## 11 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 partition.data.training 2 0 0 98.453
## 8 fit.data.training 5 0 0 338.408
## 1 cluster.data 1 0 0 14.447
## 4 fit.models 4 0 0 217.786
## 5 fit.models 4 1 1 292.236
## 9 fit.data.training 5 1 1 428.950
## 10 predict.data.new 6 0 0 441.666
## 6 fit.models 4 2 2 327.746
## 3 select.features 3 0 0 214.858
## 7 fit.models 4 3 3 335.514
## end elapsed duration
## 2 214.858 116.405 116.405
## 8 428.949 90.541 90.541
## 1 98.452 84.005 84.005
## 4 292.236 74.450 74.450
## 5 327.746 35.510 35.510
## 9 441.666 12.716 12.716
## 10 453.513 11.847 11.847
## 6 335.514 7.768 7.768
## 3 217.786 2.928 2.928
## 7 338.407 2.894 2.893
## [1] "Total Elapsed Time: 453.513 secs"